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The editors at Solutions Review curated this list of the best AI and machine learning TED talks for practitioners in the field.

TED Talks are influential videos from expert speakers in a variety of verticals. TED began in 1984 as a conference where Technology, Entertainment and Design converged, and today covers almost all topics from business to technology to global issues in more than 110 languages. TED is building a clearinghouse of free knowledge from the worlds top thinkers, and their library of videos is expansive and rapidly growing.

Solutions Review has curated this list of AI and machine learning TED talks to watch if you are a practitioner in the field. Talks were selected based on relevance, ability to add business value, and individual speaker expertise. Weve also curated TED talk lists for topics like data visualization and big data.

Erik Brynjolfsson is the director of the MIT Center for Digital Business and a research associate at the National Bureau of Economic Research. He asks how IT affects organizations, markets and the economy. His books include Wired for Innovation and Race Against the Machine. Brynjolfsson was among the first researchers to measure the productivity contributions of information and community technology (ICT) and the complementary role of organizational capital and other intangibles.

In this talk, Brynjolfsson argues that machine learning and intelligence are not the end of growth its simply the growing pains of a radically reorganized economy. A riveting case for why big innovations are ahead of us if we think of computers as our teammates. Be sure to watch the opposing viewpoint from Robert Gordon.

Jeremy Howard is the CEO ofEnlitic, an advanced machine learning company in San Francisco. Previously, he was the president and chief scientist atKaggle, a community and competition platform of over 200,000 data scientists. Howard is a faculty member atSingularity University, where he teaches data science. He is also a Young Global Leader with the World Economic Forum, and spoke at the World Economic Forum Annual Meeting 2014 on Jobs for the Machines.

Technologist Jeremy Howard shares some surprising new developments in the fast-moving field of deep learning, a technique that can give computers the ability to learn Chinese, or to recognize objects in photos, or to help think through a medical diagnosis.

Nick Bostrom is a professor at the Oxford University, where he heads theFuture of Humanity Institute, a research group of mathematicians, philosophers and scientists tasked with investigating the big picture for the human condition and its future. Bostrom was honored as one ofForeign Policys 2015Global Thinkers. His bookSuperintelligenceadvances the ominous idea that the first ultraintelligent machine is the last invention that man need ever make.

In this talk, Nick Bostrom calls machine intelligence the last invention that humanity will ever need to make. Bostrom asks us to think hard about the world were building right now, driven by thinking machines. Will our smart machines help to preserve humanity and our values or will they have values of their own?

Lis work with neural networks and computer vision (with Stanfords Vision Lab) marks a significant step forward for AI research, and could lead to applications ranging from more intuitive image searches to robots able to make autonomous decisions in unfamiliar situations. Fei-Fei was honored as one ofForeign Policys 2015Global Thinkers.

This talk digs into how computers are getting smart enough to identify simple elements. Computer vision expert Fei-Fei Li describes the state of the art including the database of 15 million photos her team built to teach a computer to understand pictures and the key insights yet to come.

Anthony Goldbloom is the co-founder and CEO ofKaggle. Kaggle hosts machine learning competitions, where data scientists download data and upload solutions to difficult problems. Kaggle has a community of over 600,000 data scientists. In 2011 and 2012,Forbesnamed Anthony one of the 30 under 30 in technology; in 2013 theMIT Tech Reviewnamed him one of top 35 innovators under the age of 35, and the University of Melbourne awarded him an Alumni of Distinction Award.

This talk by Anthony Goldbloom describes some of the current use cases for machine learning, far beyond simple tasks like assessing credit risk and sorting mail.

Tufekci is a contributing opinion writer at theNew York Times, an associate professor at the School of Information and Library Science at University of North Carolina, Chapel Hill, and a faculty associate at Harvards Berkman Klein Center for Internet and Society. Her book,Twitter and Tear Gas was published in 2017 by Yale University Press.

Machine intelligence is here, and were already using it to make subjective decisions. But the complex way AI grows and improves makes it hard to understand and even harder to control. In this cautionary talk, techno-sociologist Zeynep Tufekci explains how intelligent machines can fail in ways that dont fit human error patterns and in ways we wont expect or be prepared for.

In his bookThe Business Romantic, Tim Leberecht invites us to rediscover romance, beauty and serendipity by designing products, experiences, and organizations that make us fall back in love with our work and our life. The book inspired the creation of the Business Romantic Society, a global collective of artists, developers, designers and researchers who share the mission of bringing beauty to business.

In this talk, Tim Leberecht makes the case for a new radical humanism in a time of artificial intelligence and machine learning. For the self-described business romantic, this means designing organizations and workplaces that celebrate authenticity instead of efficiency and questions instead of answers. Leberecht proposes four principles for building beautiful organizations.

Grady Booch is Chief Scientist for Software Engineering as well as Chief Scientist for Watson/M at IBM Research, where he leads IBMs research and development for embodied cognition. Having originated the term and the practice of object-oriented design, he is best known for his work in advancing the fields of software engineering and software architecture.

Grady Booch allays our worst (sci-fi induced) fears about superintelligent computers by explaining how well teach, not program, them to share our human values. Rather than worry about an unlikely existential threat, he urges us to consider how artificial intelligence will enhance human life.

Tom Gruberis a product designer, entrepreneur, and AI thought leader who uses technology to augment human intelligence. He was co-founder, CTO, and head of design for the team that created theSiri virtual assistant. At Apple for over 8 years, Tom led the Advanced Development Group that designed and prototyped new capabilities for products that bring intelligence to the interface.

This talk introduces the idea of Humanistic AI. He shares his vision for a future where AI helps us achieve superhuman performance in perception, creativity and cognitive function from turbocharging our design skills to helping us remember everything weve ever read. The idea of an AI-powered personal memory also extends to relationships, with the machine helping us reflect on our interactions with people over time.

Stuart Russell is a professor (and formerly chair) of Electrical Engineering and Computer Sciences at University of California at Berkeley. His bookArtificial Intelligence: A Modern Approach (with Peter Norvig) is the standard text in AI; it has been translated into 13 languages and is used in more than 1,300 universities in 118 countries. He also works for the United Nations, developing a new global seismic monitoring system for the nuclear-test-ban treaty.

His talk centers around the question of whether we can harness the power of superintelligent AI while also preventing the catastrophe of robotic takeover. As we move closer toward creating all-knowing machines, AI pioneer Stuart Russell is working on something a bit different: robots with uncertainty. Hear his vision for human-compatible AI that can solve problems using common sense, altruism and other human values.

Dr. Pratik Shahs research creates novel intersections between engineering, medical imaging, machine learning, and medicine to improve health and diagnose and cure diseases. Research topics include: medical imaging technologies using unorthodox artificial intelligence for early disease diagnoses; novel ethical, secure and explainable artificial intelligence based digital medicines and treatments; and point-of-care medical technologies for real world data and evidence generation to improve public health.

TED Fellow Pratik Shah is working on a clever system to do just that. Using an unorthodox AI approach, Shah has developed a technology that requires as few as 50 images to develop a working algorithm and can even use photos taken on doctors cell phones to provide a diagnosis. Learn more about how this new way to analyze medical information could lead to earlier detection of life-threatening illnesses and bring AI-assisted diagnosis to more health care settings worldwide.

Margaret Mitchells research involves vision-language and grounded language generation, focusing on how to evolve artificial intelligence towards positive goals. Her work combines computer vision, natural language processing, social media as well as many statistical methods and insights from cognitive science. Before Google, Mitchell was a founding member of Microsoft Researchs Cognition group, focused on advancing artificial intelligence, and a researcher in Microsoft Researchs Natural Language Processing group.

Margaret Mitchell helps develop computers that can communicate about what they see and understand. She tells a cautionary tale about the gaps, blind spots and biases we subconsciously encode into AI and asks us to consider what the technology we create today will mean for tomorrow.

Kriti Sharma is the Founder of AI for Good, an organization focused on building scalable technology solutions for social good. Sharma was recently named in theForbes 30 Under 30 list for advancements in AI. She was appointed a United Nations Young Leader in 2018 and is an advisor to both the United Nations Technology Innovation Labs and to the UK Governments Centre for Data Ethics and Innovation.

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.

Matt Beane does field research on work involving robots to help us understand the implications of intelligent machines for the broader world of work. Beane is an Assistant Professor in the Technology Management Program at the University of California, Santa Barbara and a Research Affiliate with MITs Institute for the Digital Economy. He received his PhD from the MIT Sloan School of Management.

The path to skill around the globe has been the same for thousands of years: train under an expert and take on small, easy tasks before progressing to riskier, harder ones. But right now, were handling AI in a way that blocks that path and sacrificing learning in our quest for productivity, says organizational ethnographer Matt Beane. Beane shares a vision that flips the current story into one of distributed, machine-enhanced mentorship that takes full advantage of AIs amazing capabilities while enhancing our skills at the same time.

Leila Pirhaji is the founder ofReviveMed, an AI platform that can quickly and inexpensively characterize large numbers of metabolites from the blood, urine and tissues of patients. This allows for the detection of molecular mechanisms that lead to disease and the discovery of drugs that target these disease mechanisms.

Biotech entrepreneur and TED Fellow Leila Pirhaji shares her plan to build an AI-based network to characterize metabolite patterns, better understand how disease develops and discover more effective treatments.

Janelle Shane is the owner of AIweirdness.com. Her book, You Look Like a Thing and I Love Youuses cartoons and humorous pop-culture experiments to look inside the minds of the algorithms that run our world, making artificial intelligence and machine learning both accessible and entertaining.

The danger of artificial intelligence isnt that its going to rebel against us, but that its going to do exactly what we ask it to do, says AI researcher Janelle Shane. Sharing the weird, sometimes alarming antics of AI algorithms as they try to solve human problems like creating new ice cream flavors or recognizing cars on the road Shane shows why AI doesnt yet measure up to real brains.

Sylvain Duranton is the global leader of BCG GAMMA, a unit dedicated to applying data science and advanced analytics to business. He manages a team of more than 800 data scientists and has implemented more than 50 custom AI and analytics solutions for companies across the globe.

In this talk, business technologist Sylvain Duranton advocates for a Human plus AI approach using AI systems alongside humans, not instead of them and shares the specific formula companies can adopt to successfully employ AI while keeping humans in the loop.

For more AI and machine learning TED talks, browse TEDs complete topic collection.

Timothy is Solutions Review's Senior Editor. He is a recognized thought leader and influencer in enterprise BI and data analytics. Timothy has been named a top global business journalist by Richtopia. Scoop? First initial, last name at solutionsreview dot com.

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The Best 17 AI and Machine Learning TED Talks for Practitioners - Solutions Review

NEW YORK, Feb. 5, 2020 /PRNewswire/ -- VUniverse, a personalized movie and show recommendation platform that enables users to browse their streaming services in one appa channel guide for the streaming universe, announced today it's been named one of five finalists in the AI & Machine Learning category for the 23rd annual SXSW Innovation Awards.

The SXSW Innovation Awards recognizes the most exciting tech developments in the connected world. During the showcase on Saturday, March 14, 2020, VUniverse will offer first-look demos of its platform as attendees explore this year's most transformative and forward-thinking digital projects. They'll be invited to experience how VUniverse utilizes AI to cross-reference all streaming services a user subscribes to and then delivers personalized suggestions of what to watch.

"We're honored to be recognized as a finalist for the prestigious SXSW Innovation Awards and look forward to showcasing our technology that helps users navigate the increasingly ever-changing streaming service landscape," said VUniverse co-founder Evelyn Watters-Brady. "With VUniverse, viewers will spend less time searching and more time watching their favorite movies and shows, whether it be a box office hit or an obscure indie gem."

About VUniverse VUniverse is a personalized movie and show recommendation platform that enables users to browse their streaming services in one appa channel guide for the streaming universe. Using artificial intelligence, VUniverse creates a unique taste profile for every user and serves smart lists of curated titles using mood, genre, and user-generated tags, all based on content from the user's existing subscription services. Users can also create custom watchlists and share them with friends and family.

Media Contact Jessica Cheng jessica@relativity.ventures

SOURCE VUniverse

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VUniverse Named One of Five Finalists for SXSW Innovation Awards: AI & Machine Learning Category - PRNewswire

Dublin, Feb. 04, 2020 (GLOBE NEWSWIRE) -- The "Reinforcement Learning: An Introduction to the Technology" report has been added to ResearchAndMarkets.com's offering.

These days, machine learning (ML), which is a subset of computer science, is one of the most rapidly growing fields in the technology world. It is considered to be a core field for implementing artificial intelligence (AI) and data science.

The adoption of data-intensive machine learning methods like reinforcement learning is playing a major role in decision-making across various industries such as healthcare, education, manufacturing, policing, financial modeling and marketing. The growing demand for more complex machine working is driving the demand for learning-based methods in the ML field. Reinforcement learning also presents a unique opportunity to address the dynamic behavior of systems.

This study was conducted in order to understand the current state of reinforcement learning and track its adoption along various verticals, and it seeks to put forth ways to fully exploit the benefits of this technology. This study will serve as a guide and benchmark for technology vendors, manufacturers of the hardware that supports AI, as well as the end-users who will finally use this technology. Decisionmakers will find the information useful in developing business strategies and in identifying areas for research and development.

The report includes:

Key Topics Covered

Chapter 1 Reinforcement Learning

Chapter 2 Bibliography

List of TablesTable 1: Reinforcement Learning vs. Supervised Learning vs. Unsupervised LearningTable 2: Global Machine Learning Market, by Region, Through 2024

List of FiguresFigure 1: Reinforcement Learning ProcessFigure 2: Reinforcement Learning WorkflowFigure 3: Artificial Intelligence vs. Machine Learning vs. Reinforcement LearningFigure 4: Machine Learning ApplicationsFigure 5: Types of Machine LearningFigure 6: Reinforcement Learning Market DynamicsFigure 7: Global Machine Learning Market, by Region, 2018-2024

For more information about this report visit https://www.researchandmarkets.com/r/g0ad2f

Research and Markets also offers Custom Research services providing focused, comprehensive and tailored research.

CONTACT: ResearchAndMarkets.comLaura Wood, Senior Press Managerpress@researchandmarkets.comFor E.S.T Office Hours Call 1-917-300-0470For U.S./CAN Toll Free Call 1-800-526-8630For GMT Office Hours Call +353-1-416-8900

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Reinforcement Learning (RL) Market Report & Framework, 2020: An Introduction to the Technology - Yahoo Finance

One of the biggest challenges for developers of natural language systems is accounting for the many and varied ways people express themselves. There is a reason many technology companies would rather we all spoke in simple terms, it makes humans easier to understand and narrows down the chances of machines getting it wrong.

But its hardly the engaging conversational experience that people expect of AI.

Language has evolved over many centuries. As various nations colonised and traded with other nations so our language whatever your native tongue is changed. And thanks to radio, TV, and the internet its continuing to expand every day.

Among the hundreds of new words added to the Merriam Webster dictionary in 2019 was Vacay: a shortening of vacation; Haircut: a new sense was added meaning a reduction in the value of an asset; and Dad joke: a corny pun normally told by fathers.

In a conversation, we as humans would probably be able to deduce what someone meant, even if wed never heard a word or expression before. Machines? Not so much. Or at least, not if they are reliant solely on machine learning for their natural language understanding.

While adding domain specialism such as a product name or industry terminology to an application overcomes a machine recognising some specific words, understanding all of the general everyday phrases people use in between those words is where the real challenge lies.

Most commercial natural language development tools today dont offer the intelligent, humanlike, experience that customers expect in automated conversations. One of the reasons is because they rely on pattern matching words using machine learning.

Although humans - at a basic level - pattern match words too, our brains add a much higher level of reasoning to allow us to do a better job of interpreting what the person meant by considering the words used, their order, synonyms and more, plus understanding when words such as book is being used as a verb or a noun. One might say we add our own more flexible form of linguistic modelling.

As humans, we can zoom in on the vocabulary that is relevant to the current discussion. So, when someone asks a question using a phrasing weve not heard before, we can extrapolate from what we do know, to understand what is meant. Even if weve never heard a particular word before, we can guess with a high degree of accuracy what it means.

But when it comes to machines, most statisticians will tell you that accuracy isnt a great metric. Its too easily skewed by the data its based on. Instead of accuracy, they use precision and recall. In simple terms precision is about quality. It marks the number of times you were actually correct with your prediction. Recall is about quantity, the number of times you predicted correctly out of all of the possibilities.

The vast majority of conversational AI development tools available today rely purely on machine learning. However, machine learning isnt great at precision, not without massive amounts of data on which to build its model. The end result is that the developer has to code in each and every way someone might ask a question. Not a task for the faint hearted when you consider there are at least 22 ways to say yes in the English language.

Some development tools rely on linguistic modelling, which is great at precision, because it understands sentence constructs and the common ways a particular type of question is phrased, but often doesnt stack up to machine learnings recall ability. This is because linguistic modelling is based on binary rules. They either match or they dont, which means inputs with minor deviations such as word ordering or spelling mistakes will be missed.

Machine learning on the other hand provides a probability on how much the input matches with the training data for a particular intent class and is therefore less sensitive to minor variations. Used alone, neither system is conducive to delivering a highly engaging conversation.

However, by taking a hybrid approach to conversational AI development, enterprises can benefit from the best of both worlds. Rules increase the precision of understanding, while machine learning delivers greater recall by recovering the data missed by the rules.

Not only does this significantly speed up the development process, it also allows for the application to deal with examples it has never seen before. In addition, it reduces the number of customers sent to a safety net such as a live chat agent, merely because theyve phrased their question slightly differently.

By enabling the conversational AI development platform to decide where each model is used, the performance of the conversational system can be optimised even further. Making it easier for the developer to build robust applications by automatically mixing and matching the underlying technology to achieve the best results, while allowing technology to more easily understand humans no matter what words we choose to use.

Andy Peart, CMSO, Artificial Solutions

Link:
How to handle the unexpected in conversational AI - ITProPortal