Human in the loop Machine learning and AI for the people
Paco Nathan is a unicorn. It's a cliche, but gets the point across for someone who is equally versed in discussing AI with White House officials and Microsoft product managers, working on big data pipelines and organizing and part-taking in conferences such as Strata in his role as Director, Learning Group with O'Reilly Media.
Nathan has a mix of diverse background, hands-on involvement and broad vision that enables him to engage in all of those, having been active in AI, Data Science and Software Engineering for decades. The trigger for our discussion was his Human in the Loop (HITL) framework for machine learning (ML), presented in Strata EU.
Human in the loop
HITL is a mix and match approach that may help make ML both more efficient and approchable. Nathan calls HITL a design pattern, and it combines technical approaches as well as management aspects.
HITL combines two common ML variants, supervised and unsupervised learning. In supervised learning, curated (labeled) datasets are used by ML experts to train algorithms by adjusting parameters, in order to make accurate predictions for incoming data. In unsupervised learning, the idea is that running lots of data through an algorithm will reveal some sort of structure.
The less common ML variant that HITL builds on is called semi-supervised, and an important special case of that is known as "active learning." The idea is to take an ensemble of ML models, and let them "vote" on how to label each case of input data. When the models agree, their consensus gets used, typically as an automated approach.
When the models disagree or lack confidence, decision is delegated to human experts who handle the difficult edge cases. Choices made by experts are fed back to the system to iterate on training the ML models.
Nathan says active learning works well when you have have lots of inexpensive, unlabeled data -- an abundance of data, where the cost of labeling itself is a major expense. This is a very common scenario for most organizations outside of the Big Tech circle, which is what makes it interesting.
But technology alone is not enough. What could be a realistic way to bring ML, AI, and automation to mid-market businesses?
AI for the people
In Nathan's experience, most executives are struggling to grasp what the technology could do for them and identify suitable use cases. Especially for mid-market businesses, AI may seem like a far cry. But Nathan thinks they should start as soon as possible, and not look to outsource, for a number of reasons:
We are at a point where competition is heating up, and AI is key. Companies are happy to share code, but not data. The competition is going to be about data, who has the best data to use. If you're still struggling to move data from one silo to another, it means you're behind at least 2 or 3 years.
Better allocate resources now, because in 5 years there will already be the haves and have nots. The way most mid-market businesses get on board is by seeing, and sharing experiences with, early adopters in their industry. This gets them going, and they build confidence.
Getting your data management right is table stakes - you can't talk about AI without this. Some people think they can just leapfrog to AI. I don't think there will be a SaaS model for AI that does much beyond trivialize consumer use cases. "Alexa, book me a flight" is easy, but what about "Alexa, I want to learn about Kubernetes"? It will fall apart.
O’Reilly 2019 AI Conference – Speaker Submission
This on-demand webinar covers the various ways in which artificial intelligence (AI) and machine learning (ML) are coming to dominate the cyber security landscape.
This webinar provides you with an understanding of how the various types of machine learning techniques are being applied to cyber security and how those techniques are being tailored to solve particular problems in cyber security. It also covers why using multiple artificial intelligence or machine learning-based solutions enhances a defense-in-depth approach to security and how the fundamentals of cyber defense and offense are changing due to the greater adoption of these solutions.
Talk 1: Uber’s Big Data Platform: 100+ Petabytes with Minute Latency
This talk will reflect on the challenges faced with scaling Uber’s Big Data Platform to ingest, store, and serve 100+ PB of data with minute level latency while efficiently utilizing our hardware. We will provide a behind-the-scenes look at the current data technology landscape at Uber, including various open-source technologies (e.g. Hadoop, Spark, Hive, Presto, Kafka, Avro) as well as open-sourced in-house-built solutions such as Hudi, Marmaray, etc. We'll dive into the technical aspects of how our ingestion platform was re-architected to bring in 10+ trillion events/day, with 100+ TB new data/day, at minute-level latency, how our storage platform was scaled to reliably store 100+ PB of data in the data lake, and our processing platform was designed to efficiently serve millions of queries and jobs/day while processing 1+ PB per day. You’ll leave the talk with greater insight into how data truly powers each and every Uber experience and will be inspired to re-envision your own data platform to be more extensible and scalable.
Speaker : Reza Shiftehfar (Uber)
Reza Shiftehfar currently leads Uber’s Hadoop Platform team. His team helps build and grow Uber’s reliable and scalable Big Data platform that serves petabytes of data utilizing technologies such as Apache Hadoop, Apache Hive, Apache Kafka, Apache Spark, and Presto. Reza is one of the founding engineers of Uber’s data team and helped scale Uber's data platform from a few terabytes to over 100 petabytes while reducing data latency from 24+ hours to minutes. Reza holds a Ph.D. in Computer Science from the University of Illinois, Urbana-Champaign.
Talk2 : Michelangelo PyML - Uber’s Platform for Rapid Python ML Model Development
Uber aims to leverage machine learning (ML) in product development and the day-to-day management of our business. In pursuit of this goal, hundreds of data scientists, engineers, product managers, and researchers work on ML solutions across the company. This talk will cover a brief history of Uber's machine learning platform - Michelangelo. We will take a closer look into a model life-cycle of prototyping, validation, and productionization and the importance of frictionless experience at each stage of this process. And finally, we will focus on PyML - a new extension of Michelangelo that enables faster Python ML model development and seamless integration with Uber's production infrastructure.
Speaker: Stepan Bedratiuk (Uber)
Stepan Bedratiuk is a lead engineer on Michelangelo's PyML team. His work focused on scaling model deployment pipelines and model serving services. Prior to ML platform team, Stepan worked on Uber's data platform team and helped to unify and scale the data access layer. Stepan holds B.S. and M.S. in Applied Mathematics from the Taras Shevchenko National University of Kyiv, Ukraine.
How do we build a future that doesn’t leave humans behind? Erik Brynjolfsson, co-author of Machine, Platform, Crowd argues that ‘deep learning’ technologies can do much more than routine work. So how do we harness technological progress to benefit the many, not just the few?
Watch Erik Brynjolfsson, Co-founder and Director of MIT Initiative on the Digital Economy, in our latest RSA Spotlight - the edits which take you straight to the heart of the event!
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Presented at the Matroid Scaled Machine Learning Conference 2018
scaledml.org | #scaledmlconf
So we've talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving cars, to cutting edge medical diagnosis and real-time language translation, there has been an increasing need for our computers to learn from data and apply that knowledge to make predictions and decisions. This is the heart of machine learning which sits inside the more ambitious goal of artificial intelligence. We may be a long way from self-aware computers that think just like us, but with advancements in deep learning and artificial neural networks our computers are becoming more powerful than ever.
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“Machine Learning: Living in the Age of AI,” examines the extraordinary ways in which people are interacting with AI today. Hobbyists and teenagers are now developing tech powered by machine learning and WIRED shows the impacts of AI on schoolchildren and farmers and senior citizens, as well as looking at the implications that rapidly accelerating technology can have. The film was directed by filmmaker Chris Cannucciari, produced by WIRED, and supported by McCann Worldgroup.
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Machine Learning: Living in the Age of AI | A WIRED Film
Artificial intelligence is being used to do many things from diagnosing cancer, stopping the deforestation of endangered rainforests, helping farmers in India with crop insurance, it help you find the Fyre Fest Documentary on Netflix (or Hulu), or it can even be used to help you save money on your energy bill.
But how could something so helpful be racist?
Become an Inevitable/Human: https://inevitablehuman.com/
Provides steps for applying Image classification & recognition with easy to follow example.
R file: https://goo.gl/fCYm19
Machine Learning videos: https://goo.gl/WHHqWP
To install EBimage package, you can run following 2 lines;
Uses TensorFlow (by Google) as backend. Includes,
- load keras and EBImage packages
- read images
- explore images and image data
- resize and reshape images
- one hot encoding
- sequential model
- compile model
- fit model
- evaluate model
- confusion matrix
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
This presentation took place at the Machine Intelligence Summit in November 2016. View more videos from the event here: http://videos.re-work.co/events/10
Why AI Must Be Biased, and How We Can Respond
Like physics and biology, computation is a natural process with natural laws. We are making radical progress in artificial intelligence because we have learnt to exploit machine learning to capture existing computational outputs developed and transmitted by humans with human culture. This powerful strategy unfortunately undermines the assumption that machined intelligence, deriving from mathematics, would be pure and neutral, providing a fairness beyond what is present in human society. In learning the set of biases that constitute a word's meaning, AI also learns patterns some of which are based on our unfair history. Addressing such prejudice requires domain-specific interventions.
Joanna J. Bryson is a transdisciplinary researcher on the structure and dynamics of human- and animal-like intelligence. Her research covers topics ranging from artificial intelligence, through autonomy and robot ethics, and on to human cooperation. She holds degrees in Psychology from Chicago (AB) and Edinburgh (MPhil), and Artificial Intelligence from Edinburgh (MSc) and MIT (ScD). She has additional professional research experience from Oxford, Harvard, and LEGO, and technical experience in Chicago's financial industry, and international organization management consultancy. Bryson is presently a Reader (associate professor) at the University of Bath, and an affiliate of Princeton's Center for Information Technology Policy.
As an emergency doctor, I often find myself in the heartbreaking position of telling patients that they are much closer to death than they knew. Without that knowledge, and therefore without a plan for the kind of death they want, people often receive aggressive, uncomfortable medical care—even when they don’t want it. The ability to predict death is the stuff of myths and legends, but it’s much closer than we think: machine intelligence can provide precise predictions on a range of critical medical outcomes, and ease a great deal of suffering in the process. But do we really want those predictions? And what does better prediction with AI mean for the medical field? Ziad Obermeyer is an Assistant Professor at Harvard Medical School and a practicing emergency physician at the Brigham and Women’s Hospital, both in Boston.
His work uses machine learning to solve critical problems in clinical medicine. As patients get older and more complex, the volume of health data grows exponentially, and it becomes harder and harder for the human mind to keep up. Dr. Obermeyer’s work is focused on applying machine learning to find hidden signals in health data, and help doctors make better decisions and drive innovations in clinical research.
He is a recipient of an Early Independence Award from the NIH Common Fund, and a faculty affiliate at ideas42, Ariadne Labs, the Institute for Quantitative Social Science at Harvard University. He holds an A.B. (magna cum laude) from Harvard and an M.Phil. from Cambridge, and worked as a consultant at McKinsey & Co. in Geneva, New Jersey, and Tokyo, before returning to Harvard for his M.D (magna cum laude). This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
Joscha Bach - From Computation to Consciousness
Sir Roger Penrose - How can Consciousness Arise Within the Law of Physics?
July 25th, 2017
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More about Watson IoT: http://ibm.com/iot
PyData London 2018
Machine learning and data science applications can be unintentionally biased if care is not taken to evaluate their effect on different sub-populations. However, by using a "fair" approach, machine decision making can potentially be less biased than human decision makers.
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
This presentation was recorded at GOTO Amsterdam 2017
David Stibbe - Consultant at Quintor
In this session we introduce the basics about Machine Learning, explain what it is and how it relates to terms like Big Data and Artificial Intelligence.
We’ll show the various machine learning platforms that are used today like Watson, Tensorflow and Deepmind, and illustrate this [...]
Download slides and read the full abstract here:
#DataScience #ML #DeepLearning
Let’s separate the hype from reality and see what exactly machine learning (ML), deep learning (DL) and artificial intelligence (AI) algorithms can do right now in cybersecurity. We will look how different tasks, such as prediction, classification, clustering and recommendation, are applicable to the ones for attackers, such as captcha bypass and phishing, and for defenders, such as anomaly detection and attack protection. Speaking about the icing on the cake, we will cover the latest techniques of hacking security and non-security products that use ML and why its super hard to protect them against adversarial examples and other attacks.
Alexander is a co-founder of ERPScan, the president of EAS-SEC.org, an organization focused on enterprise application security, and a member of Forbes Technology Council. He has been recognized as R&D Professional of the Year by 2013. His expertise covers the security of enterprise business-critical software and includes ERP, industry-specific solutions and adopting Machine Learning and Deep learning inventions to cybersecurity problems. He has presented his research at over 100 conferences such as BlackHat, HITB, RSA held in more than 20 countries in all continents. He has held customized trainings for CISOs of Fortune 2000 companies.
Oded: “Decision-making is an important role in most businesses in the last decade. More and more tools based on artificial intelligence and machine learning are introduced to support these decisions.
The artificial intelligence and machine learning one-day program is designed for senior executives and for corporate decision makers who already invested or consider to invest in artificial intelligence and machine learning software.”
Fabrizio: “For the professional success of managers and competitive advantage of companies the interaction between human decision-making and machine learning is going to be crucial in the future and this day will keep you with all the knowledge required to prosper and benefit.”
Oded: “The format of a day AI and machine learning one day program is a mix of lectures, introduction for theories, discussion of applications and discussion of case studies. It will be done by experts from academia and by practitioners who are coming from array of industries with huge experience.
The benefits of managers for completing their artificial intelligence and machine learning program is their better understanding of their artificial intelligence and machine learning analytical tools. These tools are designed to support decisions.”
Fabrizio: “This program is designed for both multinational global corporates as well as smaller fast growing disruptors in multiple industries. As a result of the cost of these technologies dramatically reducing it is now economical feasible for big and smaller companies to leverage them for their own benefit.”
Oded: “There is sometimes a gap between what managers think they can generate from it from what actually is generated from these tools.”
Fabrizio: “Senior executives after attending this program will be able to manage digital transformations in a much more effective way because they will be able to tell the difference between just collecting data a few hundred pounds month of cost to managing large digital transformation projects at the few hundred thousand pounds of millions of pounds a month and this program will enable them to understand what to do, how and when.”
Intelligent technology smart animal husbandry farming. Modern cow transportation machines, farm, latest technology. Cow feeding - hay bales, straw, silage. World Amazing Modern Agriculture Equipment and Mega Machines, oversize load etc. Videos from EU: Great Britain, Italy, Spain, Germany, France etc. Watch!
Intelligent Technology Modern Funny Cow Transportation, Feeding, Smart Farming Automatic Mega Machine, Biggest Carriers And Trucks Oversize Load
Интеллектуальные технологии, разумное животноводство и фермерство. Современные технологии транспортировки коров, приспособление для кормления коров, современные оборудованные грузовики для перевозки скота. Смешные приколы с коровами и быками. Видео из ЕС: Великобритания, Италия, Франция, Испания, Германия и др. Смотрим!
Inteligente tecnología inteligente Agricultura Automático vaca ordeño máquina alimentación limpieza
Tecnología Inteligente Moderno Transporte de Vacas Mega Machine Biggest Truck Oversize Load
Tecnología inteligente de la ganadería inteligente de cría. Máquinas modernas de transporte de vacas, granja, la última tecnología. Alimentación de la vaca - pacas de heno, paja, ensilaje.
This video goes through an example of using TensorFlow for image recognition. Ubuntu is used via virtualbox on a Windows machine.
Business people have to make many decisions. Slowly though, machine learning is getting better at making many of these decisions. Will there be a point when human decision making is not required? This is the topic I explore in this short video.
Uber Engineering is committed to developing technologies that create seamless, impactful experiences for our customers. We are increasingly investing in Machine Learning to fulfill this vision. At Uber, our contribution to this space is Michelangelo, an internal ML-as-a-service platform that democratizes machine learning and makes scaling AI to meet the needs of the business as easy as requesting a ride.
In this talk, I’ll go over some of Uber’s early challenges at applying ML at scale, and the context around which Michelangleo was born. We’ll also talk about what the Michelangelo system looks like, and some important components that aim to lower the bar on applying ML at Uber.
Achal is a Sr. Software Engineer working on Michelangelo, and Deep Learning infrastructure
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.
In this session, we introduce MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
In this deep-dive session, through a complete ML model life-cycle example, you will walk away with:
MLflow concepts and abstractions for models, experiments, and projects
How to get started with MLFlow
Understand aspects of MLflow APIs
Using tracking APIs during model training
Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
Package, save, and deploy an MLflow model
Serve it using MLflow REST API
What’s next and how to contribute
Ahmed will present to us the algorithm that can define the creativity level in art works. As well, he will also try to answer the question whether AI can replace the artist in the future.
Ahmed nám predstaví algoritmus, ktorý dokáže určiť mieru kreativity v umeleckých dielach. Taktiež sa pokúsi zodpovedať otázku, či môže v budúcnosti umelá inteligencia nahradiť umelca. Dr. Ahmed Elgammal is a professor at the Department of Computer Science and Executive Council Faculty at the Center for Cognitive Science at Rutgers University. He is the founder and director of the Art and Artificial Intelligence Laboratory at Rutgers. Dr. Elgammal is also the founder and CEO of Artrendex, a startup that builds innovative AI technology for the creative domain. Prof. Elgammal published over 160 peer-reviewed papers, book chapters, and books in the fields of computer vision, machine learning, and artificial intelligence. His research on knowledge discovery in art history and AI art generation, received wide international media attention, including reports on the Washington Post, New York Times, NBC News, the Daily Telegraph, Science News, New Scientist, and many others. In 2017, an Artsy editorial acclaimed his work on AI generated art as “the biggest artistic achievement of the year”. In 2016, a TV segment about his research, produced for PBS, has won an Emmy award. He received the National Science Foundation CAREER Award in 2006. Dr. Elgammal received his M.Sc. and Ph.D. degrees in computer science from the University of Maryland, College Park, in 2000 and 2002, respectively.
Dr. Ahmed Elgammal je profesorom na Katedre informatiky a fakulte Výkonnej rady v Centre pre kognitívne vedy na Rutgers University. Je zakladateľom a riaditeľom laboratória pre umenie a umelú inteligenciu na univerzite Rutgers. Dr. Elgammal je zakladateľom a súčasne generálnym riaditeľom spoločnosti Artrendex, čo je start-up, ktorý vytvára inovatívnu technológiu AI pre kreatívnu oblasť. Profesor Elgammal publikoval viac ako 160 recenzovaných článkov, kapitol kníh a samotné knihy v oblastiach počítačového videnia, učenia strojov a umelej inteligencie. Jeho výskum o objavovaní vedomostí v dejinách umenia a generoví umenia umelou inteligenciou získal širokú pozornosť medzinárodných médií vrátane Washington Post, New York Times, NBC News, Daily Telegraph, Science News, New Scientist a mnohých ďalších. V roku 2017 deklarovalo vydavateľstvo Artsy jeho prácu o dielach generovaných umelou inteligenciou ako “najväčší umelecký úspech roka”. V roku 2016 získal televízny segment o jeho výskume, produkovaný pre PBS, ocenenie Emmy. V roku 2006 získal Cenu National Science Foundation CAREER. Dr Elgammal získal titul M.Sc. a Ph.D. stupňa v informatike z University of Maryland, College Park, v roku 2000 a 2002, v uvedenom poradí. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
Una antropologia del rapporto uomo-macchina, con molti temi aperti e controversi. Estratti della conferenza spettacolo tenuta a Padova, all'interno del Palazzo della Ragione, l'11 aprile 2019, nell'ambito dell'evento "L'uomo nella rivoluzione digitale".
Abbiamo trasferito molte competenze alle macchine, siamo usciti dal Paleodigitale. Questa inevitabile consegna sta avvenendo con molti vantaggi, molti punti di domanda e qualche tragedia. Quando sbagliano, gli algoritmi non hanno colpe, la responsabilità dei loro esiti è sempre degli umani che li hanno pensati. Auspicabilmente, le macchine saranno non soltanto user friendly ma più human friendly. Una "augmented lecture" per ragionare in termini moderatamente ottimistici sui nuovi ecosistemi elettronici.
Organizzazione: Assindustria Venetocentro - comprenderexcambiare 2019
Artificial Intelligence may be the single most disruptive technology the world has seen since the Industrial Revolution. But is AI real? How are companies adopting and applying AI in their organizations today? What can you do to reimagine work for the age of AI? What will the role of people be? There’s a lot of talk and concern around people losing their jobs, but on the positive side - how can machines and humans work side-by-side? How will AI be paired with humans and how can business leaders help in this transition. Responsible AI – how can we balance the opportunity with the challenges when it comes to AI? What steps can governments and businesses take?
Intelligent real time applications are a game changer in any industry. This session explains how companies from different industries build intelligent real time applications. The first part of this session explains how to build analytic models with R, Python or Scala leveraging open source machine learning / deep learning frameworks like TensorFlow, DeepLearning4J or H2O.ai. The second part discusses the deployment of these built analytic models to your own applications or microservices by leveraging the Apache Kafka cluster and Kafka’s Streams API instead of setting up a new, complex stream processing cluster. The session focuses on live demos and teaches lessons learned for executing analytic models in a highly scalable, mission-critical and performant way.
Key takeaways for the audience:
- Insights are hidden in Historical Data on Big Data Platforms such as Hadoop
- Machine Learning and Deep Learning find these Insights by building Analytics Models
- Streaming Analytics uses these Models (without Redeveloping) to act in Real Time
- See different open source frameworks for Machine Learning and Stream Processing like TensorFlow, DeepLearning4J or H2O.ai
- Understand how to leverage Kafka Streams to use analytic models in your own streaming microservices
- Learn best practices for building and deploying analytic models in real time leveraging the open source Apache Kafka Streams platform
You can find the Java code examples and analytic models for H2O and TensorFlow in my Github project: https://github.com/kaiwaehner/kafka-streams-machine-learning-examples
Confluent, founded by the creators of Apache Kafka®, enables organizations to harness business value of live data. The Confluent Platform manages the barrage of stream data and makes it available throughout an organization. It provides various industries, from retail, logistics and manufacturing, to financial services and online social networking, a scalable, unified, real-time data pipeline that enables applications ranging from large volume data integration to big data analysis with Hadoop to real-time stream processing. To learn more, please visit http://confluent.io
Mobile compute platforms provide an exciting vehicle for the deployment of new computer vision and deep learning applications. This webinar elaborates on real industry use-cases where the adoption of optimized low-level primitives for ARM processors has enabled improved performance and optimal use of heterogeneous system resources.
Best Machine Learning book: https://amzn.to/2MilWH0 (Fundamentals Of Machine Learning for Predictive Data Analytics).
Machine Learning and Predictive Analytics. #MachineLearning
Big Data, Hadoop, Federation is 5th video in this machine learning course. This video explains some of the machine learning platforms and technologies that are used. Keep in mind that these are the foundations, so we go into the types of infrastructures rather than specific products or vendors. The topics covered are big data, Hadoop, and federation. These are all terms that are very useful in predictive analytics.
This online course covers big data analytics stages using machine learning and predictive analytics. Big data and predictive analytics is one of the most popular applications of machine learning and is foundational to getting deeper insights from data. Starting off, this course will cover machine learning algorithms, supervised learning, data planning, data cleaning, data visualization, models, and more. This self paced series is perfect if you are pursuing an online computer science degree, online data science degree, online artificial intelligence degree, or if you just want to get more machine learning experience. Enjoy! Check out the entire series here: https://www.youtube.com/playlist?list=PL_c9BZzLwBRIPaKlO5huuWQdcM3iYqF2w&playnext=1
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Developing machine learning capabilities will require heavy investment and the cultivation of a generation of developers with a background in data science.
Machine learning and artificial intelligence were the stuff of science fiction when an intelligent computer turned on its creators in 2001: A Space Odyssey. Fifty years later, intelligent algorithms are beginning to reshape many facets of health care, education and commerce – and that process is just beginning, says Jia Li, the head of R&D at Google Cloud AI.
“But machine learning development is a very complex and resource-consuming process. It will require investment and expertise in every single step: Collect the data, design a model, tune model parameters, evaluate, deploy it, and finally update and iterate the entire process,” Li said during her presentation at this year’s Women in Data Science (WiDS) conference at Stanford University.
AI, or artificial intelligence, has the potential to improve the outcome for patients and help clinicians make better decisions, she says. In a sense, AI can help medical teams connect the dots. AI could suggest guidance on everything from patient lifestyles to medications and provide automated monitoring and early assessment of critical conditions by noticing subtle signals that a human would not be able to detect.
Studies have shown that 10 percent of thoracic patient deaths are related to diagnostic errors, and 4 percent of the 400 million or so radiological interpretations conducted each year in the U.S. contain clinically significant errors. Machine learning could improve those outcomes, but developing and training the software is quite challenging, Li says.
Building the models needed to make the software accurate requires board-certified radiologists to label and classify the information in those X-rays, a costly and time-consuming process. Li says that she and other data scientists are working to develop models that are less labor intensive.
In education, artificial intelligence algorithms could help customize courses for individual students based on their past experience, strengths, weaknesses and personal preferences, Li says. AI could free up teachers to work with students by automating chores such as homework and exam assessment.
Although AI and machine learning are hot topics, there are only about one million developers who have a data science background, and far fewer with a background in deep learning, Li says. Google, she says, has a partial solution to the dearth of qualified AI developers: Cloud AutoML is a suite of products that enable developers to train high-quality machine learning and AI models even if they lack expertise in those areas.
Hey guys and welcome to another fun and easy Machine Learning Tutorial on Artificial Neural Networks.
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Deep learning and Neural Networks are probably one of the hottest tech topics right now. Large corporations and young startups alike are all gold-rushing this state of the art field. If you think big data is important, then you should care about deep learning. Deep Learning (DL) and Neural Network (NN) is currently driving some of the most ingenious inventions this century. Their incredible ability to learn from data and the environment makes them the first choice for machine learning scientists.
Deep Learning and Neural Network lies in the heart of products such as self-driving cars, image recognition software, recommender systems and the list goes on. Evidently, being a powerful algorithm, it is highly adaptive to various data types as well.
People think neural network is an extremely difficult topic to learn. Therefore, either some of them don’t use it, or the ones who use it, use it as a black box. Is there any point in doing something without knowing how is it done? NO! That’s why you’ve’ come to right place at Augmented Startups to Learn about Artificial Neural Networks, so sit back relax and see how deep the rabbit hole goes.
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