“Amazon and Ocado have given the rest of the retail sector a real kick up the ass when it comes to applying innovative tech,” explains Callum Staff, lead data scientist at Marks & Spencer (M&S). “Retail businesses are having to be transformational in at least parts of their business models to survive, and I think data science is increasingly forming a big part of that.”

Staff (left), who has been at M&S for just under 12 months having previously been in the civil service, splits his time between managerial duties and getting his hands dirty. In a field which is so evidently fast-moving, he notes, anyone who spends too much time away from the frontline will be none the wiser for it.

“I set the team up just under a year ago, and so a huge part of my role has been identifying areas of value for us to support in and embedding a data drive culture in the food business,” says Staff. “Despite being in a managerial role, I think it’s absolutely vital to still get stuck in with doing the analysis – the data analysis space is moving so fast with tools and techniques at the moment that it’s easy for managers to fall behind,” he adds, “so this is my way of not doing so.”

So what has Marks & Spencer been doing in this arena? At the beginning of 2018, the retailer announced an ambitious five year ‘technology transformation’ plan, which the company said at the time would be “designed to create a more agile, faster and commercial technology function that will work with the business to deliver growth.” In July, M&S announced more than 1,000 of its employees would be ‘upskilled’ to create the world’s first ‘data academy’ in retail.

For Staff, at the frontline, it’s all about deploying machine learning models to drive greater efficiency and value to the business. “Using machine learning for one-off research purposes is cool and interesting but deploying models or the outputs of models within apps is where it’s at,” he says. “Automating decision making is where the power of data science truly comes into its own and adds organisational value.”

What’s more, it was a driving factor in Staff joining M&S. Despite ‘loving’ working in the civil service and praising its work, the need to not be tied in to long-term projects was key. “The civil service gave me a really [good] understanding of what it meant to work on truly large-scale projects. It’s also doing a lot of great work in the data science and analysis space in terms of modernising,” says Staff.

“At the stage I’m at in my career I wanted to be working at a place where I could truly ‘fail fast’ – be able to test models and technologies quickly and learn from them, and government still has its hands tied in that area in a way I’ve found M&S doesn’t,” he adds.

“However, what I’ve noticed is data quality is a challenge the world over – if anyone tells you that the government data is rubbish compared to the private sector, don’t believe it!”

“Using machine learning for one-off research is cool and interesting – but deploying models or the outputs of models within apps is where it’s at”

This makes for interesting reading compared to when sister publication IoT News spoke with Johan Krebbers, IT CTO at Shell, last year. His view was that quality of data was not so much of a problem as opposed to waiting for perfect data to arrive. “I’m less worried about quality of data because you can never get good quality of data,” he said in June. “You have to use the data you have today, start using it, make it visible, and then start improving.”

Another aspect which was important to Staff when joining M&S was around each party’s expectations. Writing last March, Jonny Brooks-Bartlett, data scientist at Deliveroo, noted frustrations in the industry around what employers need and employees want. “The data scientist likely came in to write smart machine learning algorithms to drive insight but can’t do this because their first job is to sort out the data infrastructure and/or create analytic reports,” Brooks-Bartlett wrote. “In contrast the company only wanted a chart that they could present in their board meeting each day.”

Invariably, the end goals of both are wide of the mark. Recruiters, with the aim of smoothing over any issues, can sometimes make things more difficult. Mark Miller, creator of the All Day DevOps online events, recently noticed a job advert which asked for at least five to seven years’ experience of Kubernetes in production. Kubernetes was originally released in July 2015.

Staff is no stranger to this – one recruiter once excitedly told him he would be ‘neural networking’ in a particular placement – but notes these occupational hazards are unfortunately available in most areas.

“In any industry where society suddenly sees the value there’s going to be an explosion in demand,” he says. “Organisations will ‘over-data science’ roles in order to make them sound more appealing to candidates, and candidates will over-sell themselves in order to be snapped up.

“What drew me to M&S is that they didn’t oversell – they sold it on the fact it would be carte blanche and I’d be expected to find the opportunities,” Staff adds. “It was a challenge and a risk definitely, but exciting and full of potential rewards too.”

Staff is speaking at the AI & Big Data Expo in London on April 25-26 alongside Ocado – they of the aforementioned ass kicking – with an intriguing panel session set to focus on self-service big data tools and unlocking unstructured data to create learnable features. Find out more about the event by visiting here.

Picture credit: “Marks And Spencer Department Store – Norwich – England”, by Suzy Hazelwood, used under CC BY-NC 2.0

Interested in hearing industry leaders discuss subjects like this and their use cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech ExpoBlockchain Expo, and Cyber Security & Cloud Expo.

The post Callum Staff, Marks & Spencer: On the need to fail fast, machine learning models and universal data quality challenges appeared first on AI News.

“Amazon and Ocado have given the rest of the retail sector a real kick up the ass when it comes to applying innovative tech,” explains Callum Staff, lead data scientist at Marks & Spencer (M&S). “Retail businesses are having to be transformational in at least parts of their business models to survive, and I think data science is increasingly forming a big part of that.”

Staff (left), who has been at M&S for just under 12 months having previously been in the civil service, splits his time between managerial duties and getting his hands dirty. In a field which is so evidently fast-moving, he notes, anyone who spends too much time away from the frontline will be none the wiser for it.

“I set the team up just under a year ago, and so a huge part of my role has been identifying areas of value for us to support in and embedding a data drive culture in the food business,” says Staff. “Despite being in a managerial role, I think it’s absolutely vital to still get stuck in with doing the analysis – the data analysis space is moving so fast with tools and techniques at the moment that it’s easy for managers to fall behind,” he adds, “so this is my way of not doing so.”

So what has Marks & Spencer been doing in this arena? At the beginning of 2018, the retailer announced an ambitious five year ‘technology transformation’ plan, which the company said at the time would be “designed to create a more agile, faster and commercial technology function that will work with the business to deliver growth.” In July, M&S announced more than 1,000 of its employees would be ‘upskilled’ to create the world’s first ‘data academy’ in retail.

For Staff, at the frontline, it’s all about deploying machine learning models to drive greater efficiency and value to the business. “Using machine learning for one-off research purposes is cool and interesting but deploying models or the outputs of models within apps is where it’s at,” he says. “Automating decision making is where the power of data science truly comes into its own and adds organisational value.”

What’s more, it was a driving factor in Staff joining M&S. Despite ‘loving’ working in the civil service and praising its work, the need to not be tied in to long-term projects was key. “The civil service gave me a really [good] understanding of what it meant to work on truly large-scale projects. It’s also doing a lot of great work in the data science and analysis space in terms of modernising,” says Staff.

“At the stage I’m at in my career I wanted to be working at a place where I could truly ‘fail fast’ – be able to test models and technologies quickly and learn from them, and government still has its hands tied in that area in a way I’ve found M&S doesn’t,” he adds.

“However, what I’ve noticed is data quality is a challenge the world over – if anyone tells you that the government data is rubbish compared to the private sector, don’t believe it!”

“Using machine learning for one-off research is cool and interesting – but deploying models or the outputs of models within apps is where it’s at”

This makes for interesting reading compared to when sister publication IoT News spoke with Johan Krebbers, IT CTO at Shell, last year. His view was that quality of data was not so much of a problem as opposed to waiting for perfect data to arrive. “I’m less worried about quality of data because you can never get good quality of data,” he said in June. “You have to use the data you have today, start using it, make it visible, and then start improving.”

Another aspect which was important to Staff when joining M&S was around each party’s expectations. Writing last March, Jonny Brooks-Bartlett, data scientist at Deliveroo, noted frustrations in the industry around what employers need and employees want. “The data scientist likely came in to write smart machine learning algorithms to drive insight but can’t do this because their first job is to sort out the data infrastructure and/or create analytic reports,” Brooks-Bartlett wrote. “In contrast the company only wanted a chart that they could present in their board meeting each day.”

Invariably, the end goals of both are wide of the mark. Recruiters, with the aim of smoothing over any issues, can sometimes make things more difficult. Mark Miller, creator of the All Day DevOps online events, recently noticed a job advert which asked for at least five to seven years’ experience of Kubernetes in production. Kubernetes was originally released in July 2015.

Staff is no stranger to this – one recruiter once excitedly told him he would be ‘neural networking’ in a particular placement – but notes these occupational hazards are unfortunately available in most areas.

“In any industry where society suddenly sees the value there’s going to be an explosion in demand,” he says. “Organisations will ‘over-data science’ roles in order to make them sound more appealing to candidates, and candidates will over-sell themselves in order to be snapped up.

“What drew me to M&S is that they didn’t oversell – they sold it on the fact it would be carte blanche and I’d be expected to find the opportunities,” Staff adds. “It was a challenge and a risk definitely, but exciting and full of potential rewards too.”

Staff is speaking at the AI & Big Data Expo in London on April 25-26 alongside Ocado – they of the aforementioned ass kicking – with an intriguing panel session set to focus on self-service big data tools and unlocking unstructured data to create learnable features. Find out more about the event by visiting here.

Picture credit: “Marks And Spencer Department Store – Norwich – England”, by Suzy Hazelwood, used under CC BY-NC 2.0

Interested in hearing industry leaders discuss subjects like this and their use cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech ExpoBlockchain Expo, and Cyber Security & Cloud Expo.

The post Callum Staff, Marks & Spencer: On the need to fail fast, machine learning models and universal data quality challenges appeared first on AI News.

From a science fiction dream to a critical part of our everyday lives, artificial intelligence is everywhere. You probably don't see AI at work, and that's by design. AI is changing everything. But do we want it to? (More)

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In June 2015, Elon Musk asked Urban if he would be willing to write about his companies and their surrounding industries, leading to a five-part series of Wait But Why posts on Elon Musk and his companies. Urban interviewed Musk multiple times, and the two discussed the importance of sustainable transport, solar energy, and the future of space exploration. (More)

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James Williams may not be a household name yet in most tech circles, but he will be.

For this second in what will be a regular series of conversations exploring the ethics of the technology industry, I was delighted to be able to turn to one of our current generation’s most important young philosophers of tech.

Around a decade ago, Williams won the Founder’s Award, Google’s highest honor for its employees. Then in 2017, he won an even rarer award, this time for his scorching criticism of the entire digital technology industry in which he had worked so successfully. The inaugural winner of Cambridge University’s $100,000 “Nine Dots Prize” for original thinking, Williams was recognized for the fruits of his doctoral research at Oxford University, on how “digital technologies are making all forms of politics worth having impossible, as they privilege our impulses over our intentions and are designed to exploit our psychological vulnerabilities in order to direct us toward goals that may or may not align with our own.” In 2018, he published his brilliantly written book Stand Out of Our Light, an instant classic in the field of tech ethics.

In an in-depth conversation by phone and email, edited below for length and clarity, Williams told me about how and why our attention is under profound assault. At one point, he points out that the artificial intelligence which beat the world champion at the game Go is now aimed squarely — and rather successfully — at beating us, or at least convincing us to watch more YouTube videos and stay on our phones a lot longer than we otherwise would. And while most of us have sort of observed and lamented this phenomenon, Williams believes the consequences of things like smartphone compulsion could be much more dire and widespread than we realize, ultimately putting billions of people in profound danger while testing our ability to even have a human will.

It’s a chilling prospect, and yet somehow, if you read to the end of the interview, you’ll see Williams manages to end on an inspiring and hopeful note. Enjoy!

Editor’s note: this interview is approximately 5,500 words / 25 minutes read time. The first third has been ungated given the importance of this subject. To read the whole interview, be sure to join the Extra Crunch membership. ~ Danny Crichton

Introduction and background

Greg Epstein: I want to know more about your personal story. You grew up in West Texas. Then you found yourself at Google, where you won the Founder’s Award, Google’s highest honor. Then at some point you realized, “I’ve got to get out of here.” What was that journey like?

James Williams: This is going to sound neater and more intentional than it actually was, as is the case with most stories. In a lot of ways my life has been a ping-ponging back and forth between tech and the humanities, trying to bring them into some kind of conversation.

It’s the feeling that, you know, the car’s already been built, the dashboard’s been calibrated, and now to move humanity forward you just kind of have to hold the wheel straight

I spent my formative years in a town called Abilene, Texas, where my father was a university professor. It’s the kind of place where you get the day off school when the rodeo comes to town. Lots of good people there. But it’s not exactly a tech hub. Most of my tech education consisted of spending late nights, and full days in the summer, up in the university computer lab with my younger brother just messing around on the fast connection there. Later when I went to college, I started studying computer engineering, but I found that I had this itch about the broader “why” questions that on some deeper level I needed to scratch. So I changed my focus to literature.

After college, I started working at Google in their Seattle office, helping to grow their search ads business. I never, ever imagined I’d work in advertising, and there was some serious whiplash from going straight into that world after spending several hours a day reading James Joyce. Though I guess Leopold Bloom in Ulysses also works in advertising, so there’s at least some thread of a connection there. But I think what I found most compelling about the work at the time, and I guess this would have been in 2005, was the idea that we were fundamentally changing what advertising could be. If historically advertising had to be an annoying, distracting barrage on people’s attention, it didn’t have to anymore because we finally had the means to orient it around people’s actual intentions. And search, that “database of intentions,” was right at the vanguard of that change.

The adversarial persuasion machine

Photo by joe daniel price via Getty Images

Greg: So how did you end up at Oxford, studying tech ethics? What did you go there to learn about?

James: What led me to go to Oxford to study the ethics of persuasion and attention was that I didn’t see this reorientation of advertising around people’s true goals and intentions ultimately winning out across the industry. In fact, I saw something really concerning happening in the opposite direction. The old attention-grabby forms of advertising were being uncritically reimposed in the new digital environment, only now in a much more sophisticated and unrestrained manner. These attention-grabby goals, which are goals that no user anywhere has ever had for themselves, seemed to be cannibalizing the design goals of the medium itself.

In the past advertising had been described as a kind of “underwriting” of the medium, but now it seemed to be “overwriting” it. Everything was becoming an ad. My whole digital environment seemed to be transmogrifying into some weird new kind of adversarial persuasion machine. But persuasion isn’t even the right word for it. It’s something stronger than that, something more in the direction of coercion or manipulation that I still don’t think we have a good word for. When I looked around and didn’t see anybody talking about the ethics of that stuff, in particular the implications it has for human freedom, I decided to go study it myself.

Greg: How stressful of a time was that for you when you were realizing that you needed to make such a big change or that you might be making such a big change?

James: The big change being shifting to do doctoral work?

Greg: Well that, but really I’m trying to understand what it was like to go from a very high place in the tech world to becoming essentially a philosopher critic of your former work.

James: A lot of people I talked to didn’t understand why I was doing it. Friends, coworkers, I think they didn’t quite understand why it was worthy of such a big step, such a big change in my personal life to try to interrogate this question. There was a bit of, not loneliness, but a certain kind of motivational isolation, I guess. But since then, it’s certainly been heartening to see many of them come to realize why I felt it was so important. Part of that is because these questions are so much more in the foreground of societal awareness now than they were then.

Liberation in the age of attention

Greg: You write about how when you were younger you thought “there were no great political struggles left.” Now you’ve said, “The liberation of human attention may be the defining moral and political struggle of our time.” Tell me about that transition intellectually or emotionally or both. How good did you think it was back then, the world was back then, and how concerned are you now?

What you see a lot in tech design is essentially the equivalent of a circular argument about this, where someone clicks on something and then the designer will say, “Well, see, they must’ve wanted that because they clicked on it.”

James: I think a lot of people in my generation grew up with this feeling that there weren’t really any more existential threats to the liberal project left for us to fight against. It’s the feeling that, you know, the car’s already been built, the dashboard’s been calibrated, and now to move humanity forward you just kind of have to hold the wheel straight and get a good job and keep recycling and try not to crash the car as we cruise off into this ultra-stable sunset at the end of history.

What I’ve realized, though, is that this crisis of attention brought upon by adversarial persuasive design is like a bucket of mud that’s been thrown across the windshield of the car. It’s a first-order problem. Yes, we still have big problems to solve like climate change and extremism and so on. But we can’t solve them unless we can give the right kind of attention to them. In the same way that, if you have a muddy windshield, yeah, you risk veering off the road and hitting a tree or flying into a ravine. But the first thing is that you really need to clean your windshield. We can’t really do anything that matters unless we can pay attention to the stuff that matters. And our media is our windshield, and right now there’s mud all over it.

Greg: One of the terms that you either coin or use for the situation that we find ourselves in now is the age of attention.

James: I use this phrase “Age of Attention” not so much to advance it as a serious candidate for what we should call our time, but more as a rhetorical counterpoint to the phrase “Information Age.” It’s a reference to the famous observation of Herbert Simon, which I discuss in the book, that when information becomes abundant it makes attention the scarce resource.

Much of the ethical work on digital technology so far has addressed questions of information management, but far less has addressed questions of attention management. If attention is now the scarce resource so many technologies are competing for, we need to give more ethical attention to attention.

Greg: Right. I just want to make sure people understand how severe this may be, how severe you think it is. I went into your book already feeling totally distracted and surrounded by totally distracted people. But when I finished the book, and it’s one of the most marked-up books I’ve ever owned by the way, I came away with the sense of acute crisis. What is being done to our attention is affecting us profoundly as human beings. How would you characterize it?

James: Thanks for giving so much attention to the book. Yeah, these ideas have very deep roots. In the Dhammapada the Buddha says, “All that we are is a result of what we have thought.” The book of Proverbs says, “As a man thinketh in his heart, so is he.” Simone Weil wrote that “It is not we who move, but images pass before our eyes and we live them.” It seems to me that attention should really be seen as one of our most precious and fundamental capacities, cultivating it in the right way should be seen as one of the greatest goods, and injuring it should be seen as of the greatest harms.

In the book, I was interested to explore whether the language of attention can be used to talk usefully about the human will. At the end of the day I think that’s a major part of what’s at stake in the design of these persuasive systems, the success of the human will.

“Want what we want?”

Photo by Buena Vista Images via Getty Images

Greg: To translate those concerns about “the success of the human will” into simpler terms, I think the big concern here is, what happens to us as human beings if we find ourselves waking up in the morning and going to bed at night wanting things that we really only want because AI and algorithms have helped convince us we want them? For example, we want to be on our phone chiefly because it serves Samsung or Google or Facebook or whomever. Do we lose something of our humanity when we lose the ability to “want what we want?”

James: Absolutely. I mean, philosophers call these second order volitions as opposed to just first order volitions. A first order volition is, “I want to eat the piece of chocolate that’s in front of me.” But the second order volition is, “I don’t want to want to eat that piece of chocolate that’s in front of me.” Creating those second order volitions, being able to define what we want to want, requires that we have a certain capacity for reflection.

What you see a lot in tech design is essentially the equivalent of a circular argument about this, where someone clicks on something and then the designer will say, “Well, see, they must’ve wanted that because they clicked on it.” But that’s basically taking evidence of effective persuasion as evidence of intention, which is very convenient for serving design metrics and business models, but not necessarily a user’s interests.

AI and attention

STR/AFP/Getty Images

Greg: Let’s talk about AI and its role in the persuasion that you’ve been describing. You talk about, a number of times, about the AI behind the system that beat the world champion at the board game Go. I think that’s a great example and that that AI has been deployed to keep us watching YouTube longer, and that billions of dollars are literally being spent to figure out how to get us to look at one thing over another.

“Amazon and Ocado have given the rest of the retail sector a real kick up the ass when it comes to applying innovative tech,” explains Callum Staff, lead data scientist at Marks & Spencer (M&S). “Retail businesses are having to be transformational in at least parts of their business models to survive, and I think data science is increasingly forming a big part of that.”

Staff (left), who has been at M&S for just under 12 months having previously been in the civil service, splits his time between managerial duties and getting his hands dirty. In a field which is so evidently fast-moving, he notes, anyone who spends too much time away from the frontline will be none the wiser for it.

“I set the team up just under a year ago, and so a huge part of my role has been identifying areas of value for us to support in and embedding a data drive culture in the food business,” says Staff. “Despite being in a managerial role, I think it’s absolutely vital to still get stuck in with doing the analysis – the data analysis space is moving so fast with tools and techniques at the moment that it’s easy for managers to fall behind,” he adds, “so this is my way of not doing so.”

So what has Marks & Spencer been doing in this arena? At the beginning of 2018, the retailer announced an ambitious five year ‘technology transformation’ plan, which the company said at the time would be “designed to create a more agile, faster and commercial technology function that will work with the business to deliver growth.” In July, M&S announced more than 1,000 of its employees would be ‘upskilled’ to create the world’s first ‘data academy’ in retail.

For Staff, at the frontline, it’s all about deploying machine learning models to drive greater efficiency and value to the business. “Using machine learning for one-off research purposes is cool and interesting but deploying models or the outputs of models within apps is where it’s at,” he says. “Automating decision making is where the power of data science truly comes into its own and adds organisational value.”

What’s more, it was a driving factor in Staff joining M&S. Despite ‘loving’ working in the civil service and praising its work, the need to not be tied in to long-term projects was key. “The civil service gave me a really [good] understanding of what it meant to work on truly large-scale projects. It’s also doing a lot of great work in the data science and analysis space in terms of modernising,” says Staff.

“At the stage I’m at in my career I wanted to be working at a place where I could truly ‘fail fast’ – be able to test models and technologies quickly and learn from them, and government still has its hands tied in that area in a way I’ve found M&S doesn’t,” he adds.

“However, what I’ve noticed is data quality is a challenge the world over – if anyone tells you that the government data is rubbish compared to the private sector, don’t believe it!”

“Using machine learning for one-off research is cool and interesting – but deploying models or the outputs of models within apps is where it’s at”

This makes for interesting reading compared to when sister publication IoT News spoke with Johan Krebbers, IT CTO at Shell, last year. His view was that quality of data was not so much of a problem as opposed to waiting for perfect data to arrive. “I’m less worried about quality of data because you can never get good quality of data,” he said in June. “You have to use the data you have today, start using it, make it visible, and then start improving.”

Another aspect which was important to Staff when joining M&S was around each party’s expectations. Writing last March, Jonny Brooks-Bartlett, data scientist at Deliveroo, noted frustrations in the industry around what employers need and employees want. “The data scientist likely came in to write smart machine learning algorithms to drive insight but can’t do this because their first job is to sort out the data infrastructure and/or create analytic reports,” Brooks-Bartlett wrote. “In contrast the company only wanted a chart that they could present in their board meeting each day.”

Invariably, the end goals of both are wide of the mark. Recruiters, with the aim of smoothing over any issues, can sometimes make things more difficult. Mark Miller, creator of the All Day DevOps online events, recently noticed a job advert which asked for at least five to seven years’ experience of Kubernetes in production. Kubernetes was originally released in July 2015.

Staff is no stranger to this – one recruiter once excitedly told him he would be ‘neural networking’ in a particular placement – but notes these occupational hazards are unfortunately available in most areas.

“In any industry where society suddenly sees the value there’s going to be an explosion in demand,” he says. “Organisations will ‘over-data science’ roles in order to make them sound more appealing to candidates, and candidates will over-sell themselves in order to be snapped up.

“What drew me to M&S is that they didn’t oversell – they sold it on the fact it would be carte blanche and I’d be expected to find the opportunities,” Staff adds. “It was a challenge and a risk definitely, but exciting and full of potential rewards too.”

Staff is speaking at the AI & Big Data Expo in London on April 25-26 alongside Ocado – they of the aforementioned ass kicking – with an intriguing panel session set to focus on self-service big data tools and unlocking unstructured data to create learnable features. Find out more about the event by visiting here.

Picture credit: “Marks And Spencer Department Store – Norwich – England”, by Suzy Hazelwood, used under CC BY-NC 2.0

Interested in hearing industry leaders discuss subjects like this and their use cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech ExpoBlockchain Expo, and Cyber Security & Cloud Expo.

The post Callum Staff, Marks & Spencer: On the need to fail fast, machine learning models and universal data quality challenges appeared first on AI News.

IBM has caused something of a stir after releasing thousands of photos it obtained from Flickr to train its AI.

The computing giant was technically within its rights to obtain and use the photos as they were posted by users under a Creative Commons license allowing free use.

Flickr CEO Don MacAskill sent a couple of tweets on Tuesday about IBM’s use of the photos:

“We love & support photographers and their right to choose their own licenses for their work. By default, they reserve all of their rights, and have the option to loosen them if they’d like.”

“People didn’t have to opt-in to the dataset because they had already opted into the Creative Commons license. They took action. This is the way licensing works. It’s also the magic that enables artists & scientists all over the world to create & invent using CC-licensed work.”

Of course, those posting the photos – which may contain family and friends – likely never thought they’d be used for training AI.

“None of the people I photographed had any idea their images were being used in this way…It seems a little sketchy that IBM can use these pictures without saying anything to anybody,” Greg Peverill-Conti, an exec at PR firm SharpOrange, told NBC News.

IBM’s legal team authorised the use of the photos, according to a company representative.

The collection has over a million photos; including 700 from Peverill-Conti. Some of the photographers claim to have faced difficulties getting IBM to remove their photos.

Each of the photos in the ‘Diversity in Faces’ dataset is annotated with things such as the person’s gender, age, and geometric measurements. The dataset is offered only to academic researchers.

Interested in hearing industry leaders discuss subjects like this and their use cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, Blockchain Expo, and Cyber Security & Cloud Expo.

The post IBM causes a stir after releasing Flickr photos it used for AI training appeared first on AI News.

These days, terms like data science,machine learning and artificial intelligence are sometimes mentioned interchangeably, albeit incorrectly.

Even an organization offering a new technology powered by any of these may talk about their high-end data science techniques without having much knowledge about them.

Trending AI Articles:

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In this post, we’ve outlined the relation between these technologies. But first, let’s have a quick look at what each of them stands for.

1- Data science

Put simply, data science refers to the process of extraction of useful insights from data. This interdisciplinary approach merges various fields of computer science, scientific processes and methods, and statistics in order to extract data in automated ways.

In order to mine big data, which is closely associated with the field, data science uses a diverse range of techniques, tools and algorithms gleaned from the fields. Data science training advances these techniques. If you join events or meetup that organized by Data Science Courses (as Magnimind Academyetc.), you can get more information about these techniques.

How to Become a Data Scientist? - Magnimind Academy

I wrote about that here.

2- Machine learning

In machine learning (ML), statistical methods are used to empower machines to learn without being programmed explicitly.

The field focuses on letting algorithms learn from the provided data, collect insights, and make predictions on unanalyzed data based on the gathered information. In general, machine learning is based on three key models of learning algorithms:

In the first model, a dataset is present with inputs and known outputs. In the second one, the machine learns from a dataset that comes with input variables only. In reinforcement learning model, algorithms are used to select an action.

Why Learning Python Is Important For Machine Learning Aspirants? - Magnimind Academy

I wrote about that here.

3- Artificial intelligence

Though it’s a broad term, at its core, artificial intelligence (AI) refers to the process of making machines enable to simulate the human brain function.

In the modern technology landscape, artificial intelligence is divided into two key areas.

The first one is general AI, which is based on the concept that a system can handle tasks like speaking and translating, recognizing sounds and objects, performing business or social transactions etc. The other one is applied AI that refers to concepts like driverless cars.

4- How are all these fields related to each other?

The interdisciplinary field of data science uses key skills of a wide range of fields including machine learning, statistics, visualization etc. It enables us to identify meaning and appropriate information from huge volumes of data to make informed decisions in technology, science, business etc.

For a simpler view on the relation between these technologies, artificial intelligence is applied based on machine learning. And machine learning is a part of data science that draws features from algorithms and statistics to work on the data extracted from and produced by multiple resources. Thus, you can say data science merges together a bunch of algorithms obtained from machine learning to develop a solution, and during the process, lots of ideas from traditional domain expertise, statistics and mathematics are borrowed.

In other words, data science stands for an all-inclusive term that consists of aspects of ML for functionality. Interestingly, ML is also an element of artificial intelligence, where a diverse set of purpose is achieved on a whole new level. ML and AI are a part and parcel of data science. All of these are considered, you can learndata science in Silicon Valley.

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If you liked this article, I think you might be interested in this one as well…

What is the Difference Between a Data Scientist and a Data Analyst? - Magnimind Academy

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More information on this subject can be found here.

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https://medium.com/media/c43026df6fee7cdb1aab8aaf916125ea/href

Connection Between Data Science, ML and AI was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

In this Video I will show you a really awesome opportunity how you can learn about quantum computing and run your own code on a real quantum computer completely for free. (More)

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The mobile technology is getting ready for the future. Users will see various emerging trends helping businesses to collect useful data which can be used to increase the app user experience and user engagement.

Chatbots and AI is one such trending technology which is made to ease the pain that businesses are facing these days and to scale and support business teams in their relations with clients.

Currently, chatbots are present in some of the major chat applications like Slack, Telegram, Facebook Messenger, iMessage etc.

Trending AI Articles:

1. Ten trends of Artificial Intelligence (AI) in 2019
2. Bursting the Jargon bubbles — Deep Learning
3. How Can We Improve the Quality of Our Data?
4. Machine Learning using Logistic Regression in Python with Code

According to a report by Grand View Research, the worldwide chatbot market is expected to reach $1.23 billion by 2025, an annual growth rate of 24.3%. Moreover, many mobile app development companies are coming up with their new chatbot and AI mobile apps.

Another technology which is one the rise is Artificial Intelligence(AI). AI is a deep founded technology that allow businesses to collect the data from the internet, and develop analysis that can help them to take the important actions. These days, AI is slowly being integrated into mobile apps. For instance, an app like Instagram uses AI to offer contextual content to its users.

According to Gartner, the year 2018 will be the year when we will see the rise in AI based apps. There are various examples where AI has already entered the mobile app world.

When the AI is combined with chatbots, you will find out more interesting and intelligent capability for mobile solutions.

In this post, we will talk about the benefits of using chatbots and AI in mobile apps and which industries are benefited from this.

Why Chatbot and AI is Required?

The most important that comes in mind that whether chatbot and AI is important for business or not. Let’s just say, every businesses in the near future is going to use chatbot for performing day-to-day business operations.

These days, many of the businesses are having their own call centers, where people sit and engage to solve the problem of their customers. This allow businesses to offer quick feedback to their existing customers.

Let’s talk about this in a technical term. Imagine what is instead of humans, there was method where robots could perform the same task? You can easily automate the entire process using chatbots. It would make the process easy and quick.

Moreover, chatbots and AI makes any messaging app solutions complete. Earlier, you had to type the conversations but, the chatbot will reply for you. This will help you in increasing your business and make sure that you have good engagement and customer conversion.

Benefits of Chatbots and AI in Mobile Apps:

The first and the most important benefit of chatbots and AI is that the customer interaction will be more engaging and lively. The chatbots will not get bored of your chats but, they will get to know more about user’s taste and preferences.

Moreover, with the chatbots and AI, you are not required to download an app for that task. Ask Google Assistant and Siri, and it will tell you everything.

This the way chatbots and AI will work in the future. They will work like your calculator, your music player as well as your booking agent. You have a bot, and you can do anything with it. There will be no need to download so many apps on your smartphone, thus saving a lot of space.

The chatbots are your friends. They will know you better and keep recommending you new things that you can do and listen to. They will customize everything according to your taste. The users will be more excited to have chatbots in their mobile apps.

Mobile app developers can identify all the benefits of chatbots and AI that suggests seamless deployment of the chabots for messaging or other activities and faster time-to-market.

Moreover, you can easily integrate chatbots and AI in your existing apps easily. With intelligent chatbot and AI, you can use all the features easily, and add some other additional features to your mobile app.

Read More: Top 20 Mobile App Development Companies In India For SMEs & Startups | 2018–2019

Industries Where Chatbots and AI are Mostly Used

Here are some of the industries where chatbots and AI are being mostly used:

- ECommerce and Customer Service

The most common industry where chatbots and AI is mostly used is ECommerce and Customer Service industry. Due to their availability around the world to answer user queries. Chatbots and AI are going to replace this boring customer experience over phone lines or search tools for ECommerce website.

For instance, Ebay’s ShopBot has shown the potential of chatbot for helping their customers to find the ideal product they want by using digital shopping assistant.

According to Statista.com, over 30% of ECommerce companies worldwide have integrated chatbots and AI in their websites.

- Healthcare

Another industry where the chatbots and AI is utilized is the healthcare industry. With chatbot analytics it makes it easier for healthcare providers to enhance the performance of these bots. Nowadays, talking to doctor can be difficult due to their tight schedule and costly due to less-serious ailments. WIth chabots you don’t need to visit the doctor very often when your disease is not serious. Chatbots will help you in diagnosing your disease.

According to Statista.com, 10% of all medical institutes in USA are using chatbots to cure common diseases.

- News and Publishing

Another major industry where the chatbots and AI is used is news and publishing industry. Moreover, personalization is important for the success of media companies. WIth chatbots and AI it will provide them highly personalized services, this might be the ideal medium for delivering the content to viewers and readers online.

Just recently, CNN launched a new mobile app with chatbot integrated in it. Also, they added the functionality into Facebook Messenger where users can receive daily news updates.

Conclusion

Chatbots and AI are definitely adding quality to your mobile app, especially with the support for intelligence gained through AI. If you want to increase your mobile app downloads, and increase user engagement, it is time for businesses to think smart and different. Adding chatbots and AI to your existing mobile apps will give you an edge over your competitors and will help you in increasing your conversions.

There are a number of benefits associated with native chabot and AI development. Depending on the business requirement, analyze, and automate your existing business mobile apps with native chatbots and AI.

If you are not having enough knowledge about how to integrate chatbots and AI in your existing mobile apps than you should hire a mobile app development companyor hire mobile app developers for the same.

Moreover, chatbot developers will also provide you good feedback on how to integrate the chatbot and AI. Therefore, machine learning, chatbot and AI are one of the most trending technologies nowadays if you want to remain ahead of your competition than you should definitely integrate it in your business applications.

Don’t forget to give us your 👏 !

https://medium.com/media/c43026df6fee7cdb1aab8aaf916125ea/href

How AI & Chatbot Apps Are Transforming The Mobile Technology? was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

“Amazon and Ocado have given the rest of the retail sector a real kick up the ass when it comes to applying innovative tech,” explains Callum Staff, lead data scientist at Marks & Spencer (M&S). “Retail businesses are having to be transformational in at least parts of their business models to survive, and I think data science is increasingly forming a big part of that.”

Staff (left), who has been at M&S for just under 12 months having previously been in the civil service, splits his time between managerial duties and getting his hands dirty. In a field which is so evidently fast-moving, he notes, anyone who spends too much time away from the frontline will be none the wiser for it.

“I set the team up just under a year ago, and so a huge part of my role has been identifying areas of value for us to support in and embedding a data drive culture in the food business,” says Staff. “Despite being in a managerial role, I think it’s absolutely vital to still get stuck in with doing the analysis – the data analysis space is moving so fast with tools and techniques at the moment that it’s easy for managers to fall behind,” he adds, “so this is my way of not doing so.”

So what has Marks & Spencer been doing in this arena? At the beginning of 2018, the retailer announced an ambitious five year ‘technology transformation’ plan, which the company said at the time would be “designed to create a more agile, faster and commercial technology function that will work with the business to deliver growth.” In July, M&S announced more than 1,000 of its employees would be ‘upskilled’ to create the world’s first ‘data academy’ in retail.

For Staff, at the frontline, it’s all about deploying machine learning models to drive greater efficiency and value to the business. “Using machine learning for one-off research purposes is cool and interesting but deploying models or the outputs of models within apps is where it’s at,” he says. “Automating decision making is where the power of data science truly comes into its own and adds organisational value.”

What’s more, it was a driving factor in Staff joining M&S. Despite ‘loving’ working in the civil service and praising its work, the need to not be tied in to long-term projects was key. “The civil service gave me a really [good] understanding of what it meant to work on truly large-scale projects. It’s also doing a lot of great work in the data science and analysis space in terms of modernising,” says Staff.

“At the stage I’m at in my career I wanted to be working at a place where I could truly ‘fail fast’ – be able to test models and technologies quickly and learn from them, and government still has its hands tied in that area in a way I’ve found M&S doesn’t,” he adds.

“However, what I’ve noticed is data quality is a challenge the world over – if anyone tells you that the government data is rubbish compared to the private sector, don’t believe it!”

“Using machine learning for one-off research is cool and interesting – but deploying models or the outputs of models within apps is where it’s at”

This makes for interesting reading compared to when sister publication IoT News spoke with Johan Krebbers, IT CTO at Shell, last year. His view was that quality of data was not so much of a problem as opposed to waiting for perfect data to arrive. “I’m less worried about quality of data because you can never get good quality of data,” he said in June. “You have to use the data you have today, start using it, make it visible, and then start improving.”

Another aspect which was important to Staff when joining M&S was around each party’s expectations. Writing last March, Jonny Brooks-Bartlett, data scientist at Deliveroo, noted frustrations in the industry around what employers need and employees want. “The data scientist likely came in to write smart machine learning algorithms to drive insight but can’t do this because their first job is to sort out the data infrastructure and/or create analytic reports,” Brooks-Bartlett wrote. “In contrast the company only wanted a chart that they could present in their board meeting each day.”

Invariably, the end goals of both are wide of the mark. Recruiters, with the aim of smoothing over any issues, can sometimes make things more difficult. Mark Miller, creator of the All Day DevOps online events, recently noticed a job advert which asked for at least five to seven years’ experience of Kubernetes in production. Kubernetes was originally released in July 2015.

Staff is no stranger to this – one recruiter once excitedly told him he would be ‘neural networking’ in a particular placement – but notes these occupational hazards are unfortunately available in most areas.

“In any industry where society suddenly sees the value there’s going to be an explosion in demand,” he says. “Organisations will ‘over-data science’ roles in order to make them sound more appealing to candidates, and candidates will over-sell themselves in order to be snapped up.

“What drew me to M&S is that they didn’t oversell – they sold it on the fact it would be carte blanche and I’d be expected to find the opportunities,” Staff adds. “It was a challenge and a risk definitely, but exciting and full of potential rewards too.”

Staff is speaking at the AI & Big Data Expo in London on April 25-26 alongside Ocado – they of the aforementioned ass kicking – with an intriguing panel session set to focus on self-service big data tools and unlocking unstructured data to create learnable features. Find out more about the event by visiting here.

Picture credit: “Marks And Spencer Department Store – Norwich – England”, by Suzy Hazelwood, used under CC BY-NC 2.0

Interested in hearing industry leaders discuss subjects like this and their use cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech ExpoBlockchain Expo, and Cyber Security & Cloud Expo.

The post Callum Staff, Marks & Spencer: On the need to fail fast, machine learning models and universal data quality challenges appeared first on AI News.

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"Robot Doctor Headed to a Hospital near You." YouTube. YouTube, 25 July 2012. Web. 13 Jan. 2016.
"A Ride in the Google Self Driving Car." YouTube. YouTube, 27 May 2014. Web. 13 Jan. 2016.
"The Mercedes-Benz F 015 Self-driving Car." YouTube. YouTube, 9 Mar. 2015. Web. 13 Jan. 2016.
"Foxconn Replacing Workers with Robots." YouTube. YouTube, 24 July 2015. Web. 13 Jan. 2016.
"The Future of Artificial Intelligence - Up Next." YouTube. University of California TV, 1 May 2015. Web. 13 Jan. 2016.
"Robot Baxter-Rethink Robotics." YouTube. Rethink Robotics, 14 Feb. 2014. Web. 13 Jan. 2016.
"5 Most Advanced Humanoid Robots USA/JAPAN." YouTube. TopBestBox, 17 Feb. 2015. Web. 13 Jan. 2016.
"The Future of Robots and Artificial Intelligence." YouTube. CBS Sunday Morning, 14 June 2015. Web. 13 Jan. 2016.
"Beautiful Humanoid Robot With Artificial Intelligence [ 3D Animation ]."YouTube. YouTube, 30 Sept. 2013. Web. 13 Jan. 2016.
"Artificial Intelligence." YouTube. The School Of Life, 17 Aug. 2015. Web. 13 Jan. 2016.
Russell, Stuart. "Future of Artificial Intelligence and the Human Race | Stuart Russell | TEDxYouth@EB." YouTube. Tedx Talks, 14 Dec. 2015. Web. 13 Jan. 2016.
Steiner, Christopher. "Algorithms Are Taking Over The World: Christopher Steiner at TEDxOrangeCoast." YouTube. Tedx Talks, 31 Oct. 2012. Web. 13 Jan. 2016.
"The Computer That Could Be Smarter Than Us [IBM Watson]." YouTube. ColdFusion, 1 Aug. 2014. Web. 13 Jan. 2016.
"Humans Need Not Apply." YouTube. CGP Grey, 13 Aug. 2014. Web. 13 Jan. 2016.
Pistono, Federico. "Robots Will Steal Your Job, but That's OK: Federico Pistono at TEDxVienna." YouTube. TEDx Talks, 8 Dec. 2012. Web. 13 Jan. 2016.
Hanson, Robin. "The Next Great Era: Envisioning A Robot Society: Robin Hanson at TEDxTallinn." YouTube. YouTube, 29 Aug. 2013. Web. 13 Jan. 2016.
White, Jonathan. "The Singularity Is Coming -- Are You Ready | Dr. Jonathan White | TEDxEdmonton." YouTube. TEDx Talks, 29 July 2014. Web. 13 Jan. 2016.
Wess, Stefan. "Artificial Intelligence: Dream or Nightmare? | Stefan Wess | TEDxZurich." YouTube. TEDx Talks, 20 Nov. 2014. Web. 13 Jan. 2016.
Leblanc, Andre. "Artificial Intelligence and the Future | Andre LeBlanc | TEDxMoncton." YouTube. TEDx Talks, 12 Jan. 2015. Web. 13 Jan. 2016. (More)

Microsoft is launching AI courses to help business leaders understand how they can harness the technology and gain a competitive advantage.

Last week, AI News reported on a Microsoft study which highlighted that high-growth companies are over twice as likely to be using AI. 41 percent of high-growth companies are using AI compared to just 19 percent of low-growth.

This divide needs to change or small businesses risk falling even further behind. However, overall, less than two in 10 of even high-growth companies are integrating AI which presents a huge opportunity.

Microsoft is launching AI Business School which provides lectures, case studies, guides, talks, and resources from industry leaders as well as Microsoft’s own executives. The course is designed to be for people with a business background so it’s non-technical.

“There is a gap between what people want to do and the reality of what is going on in their organisations today, and the reality of whether their organisation is ready,” said Mitra Azizirad, corporate vice president for AI marketing at Microsoft.

“Developing a strategy for AI extends beyond the business issues,” she explained. “It goes all the way to the leadership, behaviours and capabilities required to instill an AI-ready culture in your organisation.”

The new ‘Business School’ is an extension of Microsoft’s existing ‘AI School’ which is a more technical course.

You can find out more about the AI Business School here.

Interested in hearing industry leaders discuss subjects like this and their use cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, Blockchain Expo, and Cyber Security & Cloud Expo.

The post Microsoft wants to get business leaders up to speed on AI appeared first on AI News.

“Amazon and Ocado have given the rest of the retail sector a real kick up the ass when it comes to applying innovative tech,” explains Callum Staff, lead data scientist at Marks & Spencer (M&S). “Retail businesses are having to be transformational in at least parts of their business models to survive, and I think data science is increasingly forming a big part of that.”

Staff (left), who has been at M&S for just under 12 months having previously been in the civil service, splits his time between managerial duties and getting his hands dirty. In a field which is so evidently fast-moving, he notes, anyone who spends too much time away from the frontline will be none the wiser for it.

“I set the team up just under a year ago, and so a huge part of my role has been identifying areas of value for us to support in and embedding a data drive culture in the food business,” says Staff. “Despite being in a managerial role, I think it’s absolutely vital to still get stuck in with doing the analysis – the data analysis space is moving so fast with tools and techniques at the moment that it’s easy for managers to fall behind,” he adds, “so this is my way of not doing so.”

So what has Marks & Spencer been doing in this arena? At the beginning of 2018, the retailer announced an ambitious five year ‘technology transformation’ plan, which the company said at the time would be “designed to create a more agile, faster and commercial technology function that will work with the business to deliver growth.” In July, M&S announced more than 1,000 of its employees would be ‘upskilled’ to create the world’s first ‘data academy’ in retail.

For Staff, at the frontline, it’s all about deploying machine learning models to drive greater efficiency and value to the business. “Using machine learning for one-off research purposes is cool and interesting but deploying models or the outputs of models within apps is where it’s at,” he says. “Automating decision making is where the power of data science truly comes into its own and adds organisational value.”

What’s more, it was a driving factor in Staff joining M&S. Despite ‘loving’ working in the civil service and praising its work, the need to not be tied in to long-term projects was key. “The civil service gave me a really [good] understanding of what it meant to work on truly large-scale projects. It’s also doing a lot of great work in the data science and analysis space in terms of modernising,” says Staff.

“At the stage I’m at in my career I wanted to be working at a place where I could truly ‘fail fast’ – be able to test models and technologies quickly and learn from them, and government still has its hands tied in that area in a way I’ve found M&S doesn’t,” he adds.

“However, what I’ve noticed is data quality is a challenge the world over – if anyone tells you that the government data is rubbish compared to the private sector, don’t believe it!”

“Using machine learning for one-off research is cool and interesting – but deploying models or the outputs of models within apps is where it’s at”

This makes for interesting reading compared to when sister publication IoT News spoke with Johan Krebbers, IT CTO at Shell, last year. His view was that quality of data was not so much of a problem as opposed to waiting for perfect data to arrive. “I’m less worried about quality of data because you can never get good quality of data,” he said in June. “You have to use the data you have today, start using it, make it visible, and then start improving.”

Another aspect which was important to Staff when joining M&S was around each party’s expectations. Writing last March, Jonny Brooks-Bartlett, data scientist at Deliveroo, noted frustrations in the industry around what employers need and employees want. “The data scientist likely came in to write smart machine learning algorithms to drive insight but can’t do this because their first job is to sort out the data infrastructure and/or create analytic reports,” Brooks-Bartlett wrote. “In contrast the company only wanted a chart that they could present in their board meeting each day.”

Invariably, the end goals of both are wide of the mark. Recruiters, with the aim of smoothing over any issues, can sometimes make things more difficult. Mark Miller, creator of the All Day DevOps online events, recently noticed a job advert which asked for at least five to seven years’ experience of Kubernetes in production. Kubernetes was originally released in July 2015.

Staff is no stranger to this – one recruiter once excitedly told him he would be ‘neural networking’ in a particular placement – but notes these occupational hazards are unfortunately available in most areas.

“In any industry where society suddenly sees the value there’s going to be an explosion in demand,” he says. “Organisations will ‘over-data science’ roles in order to make them sound more appealing to candidates, and candidates will over-sell themselves in order to be snapped up.

“What drew me to M&S is that they didn’t oversell – they sold it on the fact it would be carte blanche and I’d be expected to find the opportunities,” Staff adds. “It was a challenge and a risk definitely, but exciting and full of potential rewards too.”

Staff is speaking at the AI & Big Data Expo in London on April 25-26 alongside Ocado – they of the aforementioned ass kicking – with an intriguing panel session set to focus on self-service big data tools and unlocking unstructured data to create learnable features. Find out more about the event by visiting here.

Picture credit: “Marks And Spencer Department Store – Norwich – England”, by Suzy Hazelwood, used under CC BY-NC 2.0

Interested in hearing industry leaders discuss subjects like this and their use cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech ExpoBlockchain Expo, and Cyber Security & Cloud Expo.

The post Callum Staff, Marks & Spencer: On the need to fail fast, machine learning models and universal data quality challenges appeared first on AI News.

“Amazon and Ocado have given the rest of the retail sector a real kick up the ass when it comes to applying innovative tech,” explains Callum Staff, lead data scientist at Marks & Spencer (M&S). “Retail businesses are having to be transformational in at least parts of their business models to survive, and I think data science is increasingly forming a big part of that.”

Staff (left), who has been at M&S for just under 12 months having previously been in the civil service, splits his time between managerial duties and getting his hands dirty. In a field which is so evidently fast-moving, he notes, anyone who spends too much time away from the frontline will be none the wiser for it.

“I set the team up just under a year ago, and so a huge part of my role has been identifying areas of value for us to support in and embedding a data drive culture in the food business,” says Staff. “Despite being in a managerial role, I think it’s absolutely vital to still get stuck in with doing the analysis – the data analysis space is moving so fast with tools and techniques at the moment that it’s easy for managers to fall behind,” he adds, “so this is my way of not doing so.”

So what has Marks & Spencer been doing in this arena? At the beginning of 2018, the retailer announced an ambitious five year ‘technology transformation’ plan, which the company said at the time would be “designed to create a more agile, faster and commercial technology function that will work with the business to deliver growth.” In July, M&S announced more than 1,000 of its employees would be ‘upskilled’ to create the world’s first ‘data academy’ in retail.

For Staff, at the frontline, it’s all about deploying machine learning models to drive greater efficiency and value to the business. “Using machine learning for one-off research purposes is cool and interesting but deploying models or the outputs of models within apps is where it’s at,” he says. “Automating decision making is where the power of data science truly comes into its own and adds organisational value.”

What’s more, it was a driving factor in Staff joining M&S. Despite ‘loving’ working in the civil service and praising its work, the need to not be tied in to long-term projects was key. “The civil service gave me a really [good] understanding of what it meant to work on truly large-scale projects. It’s also doing a lot of great work in the data science and analysis space in terms of modernising,” says Staff.

“At the stage I’m at in my career I wanted to be working at a place where I could truly ‘fail fast’ – be able to test models and technologies quickly and learn from them, and government still has its hands tied in that area in a way I’ve found M&S doesn’t,” he adds.

“However, what I’ve noticed is data quality is a challenge the world over – if anyone tells you that the government data is rubbish compared to the private sector, don’t believe it!”

“Using machine learning for one-off research is cool and interesting – but deploying models or the outputs of models within apps is where it’s at”

This makes for interesting reading compared to when sister publication IoT News spoke with Johan Krebbers, IT CTO at Shell, last year. His view was that quality of data was not so much of a problem as opposed to waiting for perfect data to arrive. “I’m less worried about quality of data because you can never get good quality of data,” he said in June. “You have to use the data you have today, start using it, make it visible, and then start improving.”

Another aspect which was important to Staff when joining M&S was around each party’s expectations. Writing last March, Jonny Brooks-Bartlett, data scientist at Deliveroo, noted frustrations in the industry around what employers need and employees want. “The data scientist likely came in to write smart machine learning algorithms to drive insight but can’t do this because their first job is to sort out the data infrastructure and/or create analytic reports,” Brooks-Bartlett wrote. “In contrast the company only wanted a chart that they could present in their board meeting each day.”

Invariably, the end goals of both are wide of the mark. Recruiters, with the aim of smoothing over any issues, can sometimes make things more difficult. Mark Miller, creator of the All Day DevOps online events, recently noticed a job advert which asked for at least five to seven years’ experience of Kubernetes in production. Kubernetes was originally released in July 2015.

Staff is no stranger to this – one recruiter once excitedly told him he would be ‘neural networking’ in a particular placement – but notes these occupational hazards are unfortunately available in most areas.

“In any industry where society suddenly sees the value there’s going to be an explosion in demand,” he says. “Organisations will ‘over-data science’ roles in order to make them sound more appealing to candidates, and candidates will over-sell themselves in order to be snapped up.

“What drew me to M&S is that they didn’t oversell – they sold it on the fact it would be carte blanche and I’d be expected to find the opportunities,” Staff adds. “It was a challenge and a risk definitely, but exciting and full of potential rewards too.”

Staff is speaking at the AI & Big Data Expo in London on April 25-26 alongside Ocado – they of the aforementioned ass kicking – with an intriguing panel session set to focus on self-service big data tools and unlocking unstructured data to create learnable features. Find out more about the event by visiting here.

Picture credit: “Marks And Spencer Department Store – Norwich – England”, by Suzy Hazelwood, used under CC BY-NC 2.0

Interested in hearing industry leaders discuss subjects like this and their use cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech ExpoBlockchain Expo, and Cyber Security & Cloud Expo.

The post Callum Staff, Marks & Spencer: On the need to fail fast, machine learning models and universal data quality challenges appeared first on AI News.

Machine learning is reshaping the whole business world today. It is making devices and applications smarter than humans, allowing them to make decisions on their own and provide a better experience.

It is expected that in the coming three years, the number of businesses investing in Machine Learning will get doubled (about 64%). Machine learning, on a global scale, makes mobile platforms easier to use, improves the customer experience, maintains customer loyalty and helps create consistent omnichannel experiences.

According to Allied Market Research, it is predicted that Machine Learning as a service market will reach $5,537 million in 2023 while growing at a CAGR of 39.0% from 2017–2023.

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In this blog, we will look at some ways using which you can enhance your mobile apps via machine learning.

1) Advanced search functionality

The machine learning solutions allow its users to optimize the search in the application, offer better and more contextual results, and make the search more intuitive and less burdensome for the clients.

It is because the machine learning algorithms actually learn from customer queries and prioritize the results that interest a particular person. Moreover, Cognitive technology also helps to group DIY videos, frequently asked questions, articles, documents, and scripts into a knowledge graph in order to provide smarter self-service and immediate responses.

Modern applications allow you to gather all available information about your customers, such as search histories and typical actions. You can use this data along with behavioral data and search requests to rank products and services and ultimately show the best matching search results. Furthermore, you can update your application with spelling and voice search corrections.

2) More personalized experience

You can benefit from the continuous learning process with machine learning. Its algorithms can analyze multiple sources of information, from social media to credit ratings and top recommendations on customer devices.

Moreover, machine learning helps you classify and structure your potential customers, find an individual approach for each group of customers and adopt the tone of your content. In short, machine learning allows you to provide your users with the most relevant and engaging content and convey the impression that your application is really talking to them.

It classifies the users based on their own interests, collect this info and then decide on your app’s appearance. Furthermore, you can use machine learning for knowing the following:

-> Who your potential customers are

-> What your customers want

-> What they can afford

-> What they are searching for to buy your products

-> What preferences, pain areas and hobbies they posses

In fact, there are is a large number of marketers who are applying machine learning in all possible and imaginable ways. For example, Uber app comes under “transportation category” which uses ML to provide an estimated time of arrival, traffic conditions and cost to riders, offer real-time info in the maps to driver and more.

3) Relevant Ads

Showing the right ads to the right audience is the crucial part of advertising. As advertising is increasingly personalized, machine learning technology helps companies target personalized ads and messages more accurately. According to The Relevance Group(http://www.relevancygroup.com/shop/the-science-behind-customer-engagement), 38% of executives are already using machine learning as part of their data management platform for advertising.

Moreover, you can prevent customers from getting tired by pressing an item they just bought and probably do not need in the near future. Machine learning helps you generate ads based on data about each customer’s unique interests and purchasing trends.

It allows you to predict how a particular customer will react to a specific promotion so that you can show specific ads only to customers who are most likely to be interested in the product or service displayed. This saves time and money and improves the reputation of your brand.

For an instance, Coca-Cola follows closely how its products are represented on social networks. The company uses image recognition technology to identify when people have posted images of their products or their competitors on Instagram, Facebook, and Twitter.

Read More: Top 20 Mobile App Development Companies In India For SMEs & Startups | 2018–2019

4) Predicts user behavior

Machine learning helps marketers in understanding their user’s behavior patterns and preferences by analyzing different kinds of data viz. gender, age, search requests, location, the frequency of app usage and so on when they use any app utilizing ML.

However, you need this data as you can use it to keep different groups of clients interested in your application and improve the effectiveness of your application and your marketing efforts. Let’s suppose you discover that there are more women under 40 who use your application than men. According to this knowledge, you can take measures to attract a male audience or direct your marketing campaign to women.

Machine learning also helps create individualized recommendations that increase the client’s commitment and the time spent on their application. Let’s take a look at Amazon’s suggestion mechanism, for example. While customers browse, an automatic learning algorithm learns on the fly about their likes and dislikes.

5) More user engagement

Machine learning apps are found to be more engaging than other apps available on the store. The machine learning tools empower you to offer a range of endearing features, full customer support and give a reason to use these apps daily.

It provides sufficient support as it can easily analyze data and make real-time decisions. For assisting its customers, it provides friendly and intelligent digital assistants like AI chatbots, conversational UXs(voice assistants) for good communication.

Besides these chatty AI assistants, there are riddle bots which sends clues and knotty riddles if you get stuck while solving complicated puzzles. Snapchat is another good example which uses machine learning and augmented reality to allow its users to revamp their pictures with amazing filters.

Moreover, Machine Intelligence allows you to improve your application with a built-in translator since machine learning supports voice translation in real time.

6) Improved security

Being an effective marketing tool, machine learning can also optimize and ensure the authentication of the application. The recognition of video, audio, and voice make it possible for customers to authenticate using their biometric data, such as the face or fingerprint. Machine learning also helps you determine access rights for your customers. It is a smart decision for any type of mobile application.

Applications such as BioID and ZoOm Login make use of machine learning to allow customers to easily log in to other websites and applications with ultra-secure face authentication and selfie style. BioID even offers periocular eye recognition for partially visible faces.

Beyond the quick and secure login, there are more applications for machine learning. With automatic learning, you can count on continuous monitoring of the application without the need for constant monitoring, machine learning algorithms detect and prohibit suspicious activities. While traditional applications can only withstand known threats, machine learning systems can protect their clients from previously unidentified malware attacks in real time.

Much renowned banking and financial companies are also leveraging machine learning algorithms to inspect clients’ past transactions, social networking activities, and loan history and to determine credit ratings. In fact, ML opens up access to various impressive features viz. wallet management, shipping cost estimation, logistics optimization, BI etc. that allow brands to efficiently forecast any financial crashes or bubbles.

Conclusion:

So far we have seen how you can refine your mobile app development using Machine learning. In fact, machine learning technology can enhance your mobile application with an efficient personalization engine, state-of-the-art search mechanisms, fast and secure authentication and protection against fraud. Hence it is advisable to you to strengthen your business with a mobile application based on machine learning if you want to differentiate yourself from your competitors.

Don’t forget to give us your 👏 !

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How To Enhance Mobile Apps Using Machine Learning? was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

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Making your job work for you
The turbulence of recent years and its impact has left many people, not least in the public sector, pessimistic about the future and dire predictions are made. Rudy Karsan has a different view. He argues that, historically, we have worked out ways to improve our lives and solve our problems with ingenuity, and we will continue to do so. The foremost risk, in his opinion, lies elsewhere. In this thought provoking and unique take on the ills that plague society today, Rudy offers solutions and ideas on how humanity can move forward to ensure a better future for all.
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Artificial intelligence (AI), one of twenty core technologies I identified back in 1983 as the drivers of exponential economic value creation, has worked its way into our lives. From Amazon’s Alexa and Facebook’s M to Google’s Now and Apple’s Siri, AI is always growing — so keeping a closer eye on future developments, amazing opportunities, and predictable problems is imperative.

IBM’s Watson is a good example of a fast-developing AI system. Watson is a cognitive computer that learns over time. This cognitive AI technology can process information much more like a smart human than a smart computer. IBM Watson first shot to fame back in 2011 by beating two of Jeopardy’s greatest champions on TV. Thanks to its three unique capabilities — natural language processing; hypothesis generation and evaluation; and dynamic learning — cognitive computing is being applied in an ever-growing list of fields.

Trending AI Articles:

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Today, cognitive computing is used in a wide variety of applications, including health care, travel, and weather forecasting. When IBM acquired The Weather Company, journalists were quick to voice their amusement. However, IBM soon had the last laugh when people learned that the Weather Company’s cloud-based service could handle over 26 million inquiries every day on the organization’s website and mobile app, all while learning from the daily changes in weather and from the questions being asked. The data gleaned from the fourth most-used mobile app would whet the appetite of the permanently ravenous IBM Watson and enable IBM to increase the level of analytics for its business clients.

Weather is responsible for business losses to the tune of $500 billion a year. Pharmaceutical companies rely on accurate forecasts to predict a rise in the need for allergy medication. Farmers’ livelihoods often depend on the weather as well, not only impacting where crops can be successfully grown but also where the harvest should be sold. Consider the news that IBM followed its Weather Company purchase by snapping up Merge Healthcare Inc. for a cool $1 billion in order to integrate its imaging management platform into Watson, and the dynamic future of AI becomes more than evident.

The accounting industry can benefit from this technology, as well. When I was the keynote speaker at KPMG’s annual partner meeting, I suggested that the company consider partnering with IBM to have Watson learn all of the global accounting regulations so that they could transform their practice and gain a huge advantage. After doing their own research on the subject, the KPMG team proceeded to form an alliance with IBM’s Watson unit to develop high-tech tools for auditing, as well as for KPMG’s other lines of business.

Thanks to the cloud and the virtualization of services, no one has to own the tools in order to have access to them, allowing even smaller firms to gain an advantage in this space. Success all comes back to us humans and how creatively we use the new tools.

IBM’s Watson, along with advanced AI and analytics from Google, Facebook, and others, will gain cognitive insights mined from the ever-growing mountains of data generated by the Internet of Things (IoT) to revolutionize every industry.

Advanced AI is promising almost limitless possibilities that will enable businesses in every field to make better decisions in far less time. But at what price? Many believe the technology will lead directly to massive job cuts throughout multiple industries. and suggest that this technology is making much of the human race redundant.

It is crucial to recognize how the technological landscape is evolving before our eyes during this digital transformation. Yes, it is true that hundreds of traditional jobs are disappearing, but it’s also important to realize the wealth of new roles and employment opportunities arriving that are needed to help us progress further.

The rise of the machines started with the elimination of repetitive tasks, such as those in the manufacturing environment, and it is now moving more into white-collar jobs. The key for us is not to react to change, but to get ahead of it by paying attention to what I call the “Hard Trends” — the facts that are shaping the future — so that we can all anticipate the problems and new opportunities ahead of us. We would do well to capitalize on the areas that computers have great difficulty understanding, including collaboration, communication, problem solving, and much more. To stay ahead of the curve, we will all need to learn new things on an ongoing basis, as well as unlearn the old ways that are now holding us back. Remember, we live in a human world where relationships are all-important.

We need to be aware of the new tools available to us, and then creatively apply them to transform the impossible into the possible. By acquiring new knowledge, developing creativity and problem-solving skills, and honing our interpersonal, social, and communication skills, we can all thrive in a world of transformational change.

Are you reacting to change or paying attention to the Hard Trend facts that are shaping the future?

If you want to anticipate the problems and opportunities ahead of you, pick up a copy of my latest book, The Anticipatory Organization.

Don’t forget to give us your 👏 !

https://medium.com/media/c43026df6fee7cdb1aab8aaf916125ea/href

Artificial Intelligence: Disruption or Opportunity? was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Voice recognition is a standard part of the smartphone package these days, and a corresponding part is the delay while you wait for Siri, Alexa or Google to return your query, either correctly interpreted or horribly mangled. Google’s latest speech recognition works entirely offline, eliminating that delay altogether — though of course mangling is still an option.

The delay occurs because your voice, or some data derived from it anyway, has to travel from your phone to the servers of whoever operates the service, where it is analyzed and sent back a short time later. This can take anywhere from a handful of milliseconds to multiple entire seconds (what a nightmare!), or longer if your packets get lost in the ether.

Why not just do the voice recognition on the device? There’s nothing these companies would like more, but turning voice into text on the order of milliseconds takes quite a bit of computing power. It’s not just about hearing a sound and writing a word — understanding what someone is saying word by word involves a whole lot of context about language and intention.

Your phone could do it, for sure, but it wouldn’t be much faster than sending it off to the cloud, and it would eat up your battery. But steady advancements in the field have made it plausible to do so, and Google’s latest product makes it available to anyone with a Pixel.

Google’s work on the topic, documented in a paper here, built on previous advances to create a model small and efficient enough to fit on a phone (it’s 80 megabytes, if you’re curious), but capable of hearing and transcribing speech as you say it. No need to wait until you’ve finished a sentence to think whether you meant “their” or “there” — it figures it out on the fly.

So what’s the catch? Well, it only works in Gboard, Google’s keyboard app, and it only works on Pixels, and it only works in American English. So in a way this is just kind of a stress test for the real thing.

“Given the trends in the industry, with the convergence of specialized hardware and algorithmic improvements, we are hopeful that the techniques presented here can soon be adopted in more languages and across broader domains of application,” writes Google, as if it is the trends that need to do the hard work of localization.

Making speech recognition more responsive, and to have it work offline, is a nice development. But it’s sort of funny considering hardly any of Google’s other products work offline. Are you going to dictate into a shared document while you’re offline? Write an email? Ask for a conversion between liters and cups? You’re going to need a connection for that! Of course this will also be better on slow and spotty connections, but you have to admit it’s a little ironic.

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IBM Watson es una tecnología pionera de computación cognitiva, capaz de interaccionar con los humanos de una manera similar a como lo hacen las personas. Lee y entiende el lenguaje natural de las personas. Es capaz de responder a preguntas complejas en pocos segundos a partir de su avanzada capacidad para analizar ingente cantidad de información. Ante una pregunta, formula hipótesis y escoge la respuesta en la que tiene un mayor nivel de confianza. Muestra los pasos que ha dado para llegar a esta respuesta de una forma clara y sencilla, es decir, presenta su razonamiento. Y, además, aprende de su experiencia, así que cada vez es más inteligente. En resumen, Watson procesa la información más como un humano que como una máquina. Con este tipo de tecnología, IBM está abriendo una nueva era en la historia de la computación. Más información en: http://www.ibm.com/smarterplanet/us/en/ibmwatson/what-is-watson.html (More)

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Artificial intelligence, or what passes for it, can be found in practically every major tech company and, increasingly, in government programs. A joint Harvard-MIT program just unloaded $750,000 on projects looking to keep such AI developments well understood and well reported.

The Ethics and Governance in AI Initiative is a combination research program and grant fund operated by MIT’s Media Lab and Harvard’s Berkman-Klein Center. The small projects selected by the initiative are generally speaking aimed at using technology to keep people informed, or informing people about technology.

AI is an enabler of both good and ill in the world of news and information gathering, as the initiative’s director, Tim Hwang, said in a news release:

“On one hand, the technology offers a tremendous opportunity to improve the way we work —
including helping journalists find key information buried in mountains of public records. Yet we
are also seeing a range of negative consequences as AI becomes intertwined with the spread of
misinformation and disinformation online.”

These grants are not the first the initiative has given out, but they are the first in response to an open call for ideas, Hwang noted.

The largest sum of the bunch, a $150K grant, went to MuckRock Foundation’s project Sidekick, which uses machine learning tools to help journalists scour thousands of pages of documents for interesting data. This is critical in a day and age when government and corporate records are so voluminous (for example, millions of emails leaked or revealed via FOIA) that it is basically impossible for a reporter or even team to analyze them without help.

Along the same lines is Legal Robot, which was awarded $100K for its plan to mass-request government contracts, then extract and organize the information within. This makes a lot of sense: People I’ve talked to in this sector have told me that the problem isn’t a lack of data but a surfeit of it, and poorly kept at that. Cleaning up messy data is going to be one of the first tasks any investigator or auditor of government systems will want to do.

Tattle is a project aiming to combat disinformation and false news spreading on WhatsApp, which as we’ve seen has been a major vector for it. It plans to use its $100K to establish channels for sourcing data from users, since of course much of WhatsApp is encrypted. Connecting this data with existing fact-checking efforts could help understand and mitigate harmful information going viral.

The Rochester Institute of Technology will be using its grant (also $100K) to look into detecting manipulated video, both designing its own techniques and evaluating existing ones. Close inspection of the media will render a confidence score that can be displayed via a browser extension.

Other grants are going to AI-focused reporting work by the Seattle Times and by newsrooms in Latin America, and to workshops training local media in reporting AI and how it affects their communities.

To be clear, the initiative isn’t investing in these projects — just funding them with a handful of stipulations, Hwang explained to TechCrunch over email.

“Generally, our approach is to give grantees the freedom to experiment and run with the support that we give them,” he wrote. “We do not take any ownership stake but the products of these grants are released under open licenses to ensure the widest possible distribution to the public.”

He characterized the initiative’s grants as a way to pick up the slack that larger companies seem are leaving behind as they focus on consumer-first applications like virtual assistants.

“It’s naive to believe that the big corporate leaders in AI will ensure that these technologies are being leveraged in the public interest,” wrong Hwang. “Philanthropic funding has an important role to play in filling in the gaps and supporting initiatives that envision the possibilities for AI outside the for-profit context.”

You can read more about the initiative and its grantees here.