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AI algorithms make important decisions about you all the time — like how much you should pay for car insurance or whether or not you get that job interview. But what happens when these machines are built with human bias coded into their systems? Technologist Kriti Sharma explores how the lack of diversity in tech is creeping into our AI, offering three ways we can start making more ethical algorithms.
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A new version of Atlas, designed to operate outdoors and inside buildings. It is specialized for mobile manipulation. It is electrically powered and hydraulically actuated. It uses sensors in its body and legs to balance and LIDAR and stereo sensors in its head to avoid obstacles, assess the terrain, help with navigation and manipulate objects. This version of Atlas is about 5′ 9″ tall (about a head shorter than the DRC Atlas) and weighs 180 lbs.
Can A.I. make music? Can it feel excitement and fear? Is it alive? Will.i.am and Mark Sagar push the limits of what a machine can do. How far is too far, and how much further can we go?
In this interview, I ask Fast.AI researcher Rachel Thomas 67 questions about machine learning and her day to day life. She was selected by Forbes as one of “20 Incredible Women in AI”, was an early engineer at Uber, and earned her math PhD at Duke. She is co-founder of fast.ai, which created the “Practical Deep Learning for Coders” course that over 100,000 students have taken. Rachel is a popular writer and keynote speaker. Her writing has been read by over half a million people; has been translated into Chinese, Spanish, Korean, & Portuguese; and has made the front page of Hacker News 8x.
This project was my entry to Brown University (US) Admission Application where students had to upload a video showing their personal projects.
It showcases the current features and possibilities of the project.
It is a personal project so I am unable to share the source code. But I will drop some resources where you can learn and start working on your program. You can get a basic understanding of Chatbots and Speech Recognition through courses offered by ‘Stanford’ and ‘DeepLearning’ on Coursera and get started. I might upload a tutorial of some kind in the future showing the basic code to get started. Thank you for understanding!
A U.S. Naval War College Lecture of Opportunity from Jan. 15, 2021.
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You’ll live a lot longer than you think, thanks to the progress that medicine will make in your lifetime. And because progress is accelerating, you’ll see a lot more new things in the next decades than in your life to date.
Pedro Domingos takes you on a whirlwind tour of the new superpowers you’ll acquire, the technologies that will underpin them, and what you’ll do with them. Fasten your seatbelt – from controlling the world with your mind to editing your own genes, the next hundred years of your life are going to be a wild ride.
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The Glassdoor Report last year (Glassdoor’s 50 Best Jobs In America For 2018) named data scientist as the best job in the US for three years running.
The report took into consideration three key factors, namely job satisfaction rating, median annual base salary, and the number of job openings. Each of these three factors was given equal importance, and it was found that data science jobs excelled across all three.
Apart from $110,000 as a median base salary, data science jobs were found to have a job satisfaction score and a job score of 4.4 and 4.8 (out of 5) respectively.
Similar findings were made public in a related report of CareerCast.com where jobs in data science were shown to have one of the best growth rates in the industry over the next decade and continue to be one of the most difficult positions to be filled.
According to rjmetrics.com statistics, these findings were supported and it was stated that over the past four years, merely 50% of the projected 19,500 data scientist positions got filled. All these statistics and predictions indicate how popular the job of a data scientist is and will become in the coming days.
Let’s take a deeper look into certain aspects to understand the driving factors behind this trend that makes data scientist the hottest job of the 21st century.
A research conducted by Business Insider some time ago predicted that by 2020, over 24 billion internet-connected devices will get installed globally. In other words, every person on this planet will have more than four devices to use. Together, these devices comprise the Internet of Things (IoT), and its presence is permanently changing our world.
IoT can be called the link between the digital world of data and physical world inhabited by humans. From your smartphones and smartwatches, to tablets, computers, smart TVs, and wearables — all come under the IoT.
What’s more, even your everyday appliances like lights, fans, smoke detectors, and thermostats have started boasting of smart capabilities, which make them a part of the IoT. Even how you socialize, or get from one place to other (via the transportation system) is changing and will change further because of the IoT. The tech giant has been enhanced by the time over.
If you are wondering how the IoT is connected to data, here’s the answer: all these varied smart devices and appliances draw a large amount of data. A number of sources are used to collect this data, which can be categorized into two types: unstructured data and structured data, both of which come under the domain of big data.
Human input is more likely to contribute to unstructured data, which is the fastest growing type of big data.
This includes your social media posts, the emails you send and receive from various sources, the videos your stream or share, the customer reviews you post etc. since unstructured data isn’t streamlined, it’s difficult to sort and manage with technology. On the other hand, structured data is collected by products, services, and electronic devices.
For example, your website traffic data, or GPS coordinates collected by your smartphone fall under this category. Since such data is organized, usually by categories, a computer or a program can be used to read, sort, and organize it automatically because of demand in data.
A data scientist works with both structured and unstructured data, and sorts, organizes and analyses them to present them in easily understandable forms to the stakeholders. This in turn would help the stakeholders examine if their departmental, business and revenue goals are being met, and also help them take important business decisions.
In other words, a data scientist’s job isn’t just to process and analyze data.
Rather, he/she should be able to translate departmental or company goals into data-based deliverables like pattern detection analysis, prediction engines, optimization algorithms etc, which would offer the stakeholders useful insights and facilitate informed decision making.
Not having a data scientist on your team would mean that even if you sit on a pile of data, you won’t be able to leverage it for your benefit as you can’t get any meaningful insights or use them to predict trends (like the surge in interest in a particular item), which would have helped you to make timely business decisions.
In today’s competitive business landscape where data never stops flowing and the nature of challenges undergoes a continuous change, it’s the data scientists who can help decision makers make a transition from ad hoc analysis to enjoying an ongoing dialogue with data.
Now that you have an idea of the role of a data scientist, let’s see what makes it the hottest job of the 21st century.
Lack of qualified talent is one of the key reasons why data scientist jobs are in high demand.
Even for the positions that are vacant at present, employers are finding it difficult to fill them as there aren’t enough skilled and qualified people around. The problem is that though companies need more data scientists, most are still not certified yet or are still studying for their degrees.
And this gap between demand and the availability of talents is set to worsen since IBM has predicted that by 2020, the demand for data scientists will skyrocket to 28%.
This sets the prefect stage for aspirants seeking jobs as data scientists. Thanks to the huge vacancy in this field (which is set to increase further in the future), these aspirants can apply for and land such in-demand jobs a lot faster than their counterparts seeking other jobs. Thus, companies want skilled person who is worker.
As already mentioned before, the median salary of a data scientist in the US is close to $110,000. Elsewhere in the world too, the job pays extremely well.
According to Burtch Works Study: Salaries of Data Scientists, the base salary of these professionals is up to 36% higher than their counterparts working with other predictive analytics.
As the demand for competent data scientists is set to grow significantly, the salary for the post is likely to become better.
Apart from the lure of a fat paycheck, the excitement of working with the latest technologies is also a big draw.
From Artificial Intelligenceand Machine Learning (with progressive future prospects) to R and Python (considered as the most popular technologies), and MongoDB (the most popular database), a data scientist gets to work with the constant evolution of technologies.
This first-hand experience together with the future prospects of these popular technologies make the position of data scientists the most coveted one.
Once, data scientists were thought to be employable only in the IT and finance sectors, and that too in large companies. But the scenario has changed today. While the bigger names in IT, Finance and Insurance sectors continue to hire these professionals in career stage, even the medium and smaller companies are now hiring them as they have realized the importance of data-driven decision making.
Though these smaller companies don’t have a data bandwidth as large as their bigger counterparts, they have started hiring qualified data scientists, who can help them get valuable insights from their metrics. With this, these smaller and medium companies can get a comparable “big data” advantage as the larger companies, which in turn would help them stay competitive.
And the good news is that it’s no longer just the IT, Professional Services, and Finance and Insurance that offer jobs to data scientists.
From companies in telecom, e-commerce, and BFSI (banking, financial services and insurance), to transportation and more, a lot of industries that generate or have access to a massive amount of data have woken up to the potential of leveraging such data to their business advantage.
And they are now hiring data scientists to process this huge amount of data to make the most of their business decision-making potential.
Be it proactive (where you anticipate what the problem could be and try to address it before it disrupts business operations) or preventive decision making, data science professionals can help. Even spotting trends to decide on the future course of business, or steering the business to an entirely new direction (in line with changing demands, preferences etc) becomes easy with the insights generated by data science.
Automating many small decisions is another key thing which can be done easily when the right data is collected and utilized.
For example, financial institutions using automated credit scoring systems to forecast their customers ‘credit-worthiness’ would not only free their employees from the task, but also bring a higher degree of accuracy, while speeding up the process and lowering the risk of not getting a return on the loans in case the customer wasn’t worthy of being granted a loan.
As the future scope of data science is extremely bright, it’s no wonder why there’s almost a mad rush to get qualified as a data scientist and find jobs in this highly lucrative domain withdata science in 6 weeks in Silicon Valley. Data science bootcamp in Bay Area provides an advantage to be accepted for a job.https://medium.com/media/55c903f3de11f03eb2d75f931f5d7893/hrefbody[data-twttr-rendered="true"] background-color: transparent;.twitter-tweet margin: auto !important;
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The Hottest Job Of The 21st Century(Data Scientist!) was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
As the biggest sales and marketing technology firms mature, they are all turning to AI and machine learning to advance the field. This morning it was Oracle’s turn, announcing several AI-fueled features for its suite of sales tools.
Rob Tarkoff, who had previous stints at EMC, Adobe and Lithium, and is now EVP of Oracle CX Cloud says that the company has found ways to increase efficiency in the sales and marketing process by using artificial intelligence to speed up previously manual workflows, while taking advantage of all the data that is part of modern sales and marketing.
For starters, the company wants to help managers and salespeople understand the market better to identify the best prospects in the pipeline. To that end, Oracle is announcing integration with DataFox, the company it purchased last fall. The acquisition gave Oracle the ability to integrate highly detailed company profiles into their Customer Experience Cloud, including information such as SEC filings, job postings, news stories and other data about the company.
“One of the things that DataFox helps you you do better is machine learning-driven sales planning, so you can take sales and account data and optimize territory assignments,” he explained.
The company also announced an AI sales planning tool. Tarkoff says that Oracle created this tool in conjunction with its ERP team. The goal is to use machine learning to help finance make more accurate performance predictions based on internal data.
“It’s really a competitor to companies like Anaplan, where we are now in the business of helping sales leaders optimize planning and forecasting, using predictive models to identify better future trends,” Tarkoff said.
The final tool is really about increasing sales productivity by giving salespeople a virtual assistant. In this case, it’s a chatbot that can help handle tasks like scheduling meetings and offering task reminders to busy sales people, while allowing them to use their voices to enter information about calls and tasks. “We’ve invested a lot in chatbot technology, and a lot in algorithms to help our bots with specific dialogues that have sales- and marketing-industry specific schema and a lot of things that help optimize the automation in a rep’s experience working with sales planning tools,” Tarkoff said.
Brent Leary, principal at CRM Essentials, says that this kind of voice-driven assistant could make it easier to use CRM tools. “The Smarter Sales Assistant has the potential to not only improve the usability of the application, but by letting users interact with the system with their voice it should increase system usage,” he said.
All of these enhancements are designed to increase the level of automation and help sales teams run more efficiently with the ultimate goal of using data to more sales and making better use of sales personnel. They are hardly alone in this goal as competitors like Salesforce, Adobe and Microsoft are bringing a similar level of automation to their sales and marketing tools
The sales forecasting tool and the sales assistant are generally available starting today. The DataFox integration will GA in June.
Geordie Rose Interview part 4-6
Discussion about D-Wave and quantum computing
Vikash Mansinghka, MIT
Uncertainty in Computation
Time for a summary of this week in AI.
Presentation by Catherine Henry (2017 Clearwater DevCon).
When teaching a subject through text it can be beneficial to evaluate the reader’s understanding; however, the creation of relevant questions and answers can be time-consuming and tedious. I will walk through how the implementation of NLP libraries and algorithms can assist in, and potentially remove altogether, the current necessity of an individual manually formulating these tests.
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This is a special episode of the Bionic Bug Podcast streamed LIVE on YouTube on November 12 at 7:00PM EST. Join me for a riveting interview with Mr. Samuel Bennett to talk AI and Robotics. Mr. Bennett is currently a researcher at the Center for Naval Analyses and specializes in robotics and Russia.
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Recorded September 15, 2017