Technology is transforming what happens when a child goes to school

This article argues that technology-enabled 'personalized learning' offers huge promise to advance education across subjects and student circumstances. Advances in AI and other "ed tech" technologies are making it possible to craft compelling learning experiences that are responsive to a student's needs and interests.  

EC agrees with the underlying premise of 'responsive learning', namely that a student's learning experience is enriched and furthered by an active, responsive dialogue over the topic. Just as a good teacher would tailor his/her direction based on a child's response, so too should good education technology. In our pursuit to advance AI over natural human language, we believe we will make contributions relevant to the education field.
Education Technology Responsive Learning
 

This article argues that technology-enabled 'personalized learning' offers huge promise to advance education across subjects and student circumstances. Advances in AI and other "ed tech" technologies are making it possible to craft compelling learning experiences that are responsive to a student's needs and interests.  

EC agrees with the underlying premise of 'responsive learning', namely that a student's learning experience is enriched and furthered by an active, responsive dialogue over the topic. Just as a good teacher would tailor his/her direction based on a child's response, so too should good education technology. In our pursuit to advance AI over natural human language, we believe we will make contributions relevant to the education field.
Education Technology Responsive Learning
 

 

Machines as thought partners

David Ferrucci thinks big when he looks at the potential of artificial intelligence. He sees a future in which AI will become a powerful amplifier of human creative potential – a system based not on machine learning but natural learning that will enable humans and machines to explore, collaborate and ask "why" together.

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AI needs to answer "Why?" Language understanding Collaborative thought partner
PopTech
 

David Ferrucci thinks big when he looks at the potential of artificial intelligence. He sees a future in which AI will become a powerful amplifier of human creative potential – a system based not on machine learning but natural learning that will enable humans and machines to explore, collaborate and ask "why" together.

Watch Video

AI needs to answer "Why?" Language understanding Collaborative thought partner
PopTech
 

 

The Lack of Intelligence About Artificial Intelligence

This article argues that in the coming years, AI advances will lead to transition and displacement, and the emerging crisis will be about reaction and replacement policy. Lots of pundits talk about the industries most likely to be impacted by AI, but very few talk about the small number of humans who create the technology, how the technology will inevitably become just another black box appliance, or how the transition to machines will be managed.

This article supports EC's position that AI needs to be able to give us rational answers that explain outputs. If/when humans are displaced by AI, the danger is that AI will make important decisions without explanation.
AI needs to answer "Why?"
Josep Lago / AFP / Getty Images
 

This article argues that in the coming years, AI advances will lead to transition and displacement, and the emerging crisis will be about reaction and replacement policy. Lots of pundits talk about the industries most likely to be impacted by AI, but very few talk about the small number of humans who create the technology, how the technology will inevitably become just another black box appliance, or how the transition to machines will be managed.

This article supports EC's position that AI needs to be able to give us rational answers that explain outputs. If/when humans are displaced by AI, the danger is that AI will make important decisions without explanation.
AI needs to answer "Why?"
Josep Lago / AFP / Getty Images
 

 

Technology Quarterly: Finding A Voice

Computers have got much better at translation, voice recognition and speech synthesis, ... But they still don’t understand the meaning of language.

Language understanding AI needs to answer "Why?"
 

Computers have got much better at translation, voice recognition and speech synthesis, ... But they still don’t understand the meaning of language.

Language understanding AI needs to answer "Why?"
 

 

AI's Language Problem

Machines that truly understand language would be incredibly useful. But we don’t know how to build them.

Language understanding
 

Machines that truly understand language would be incredibly useful. But we don’t know how to build them.

Language understanding
 

 

The Man Who Would Teach Machines to Think

This, then, is the trillion-dollar question: Will the approach undergirding AI today—an approach that borrows little from the mind, that’s grounded instead in big data and big engineering—get us to where we want to go? How do you make a search engine that understands if you don’t know how you understand? 

Language understanding
 

This, then, is the trillion-dollar question: Will the approach undergirding AI today—an approach that borrows little from the mind, that’s grounded instead in big data and big engineering—get us to where we want to go? How do you make a search engine that understands if you don’t know how you understand? 

Language understanding
 

 

Q&A: Should artificial intelligence be legally required to explain itself?

As artificial intelligence (AI) becomes more sophisticated, it also becomes more opaque. Machine-learning algorithms can grind through massive amounts of data, generating predictions and making decisions without the ability to explain to humans what it’s doing. In matters of consequence—from hiring decisions to criminal sentencing—should we require justifications? A commentary published today in Science Robotics discusses regulatory efforts to make AI more transparent, explainable, and accountable. Science spoke with the article’s primary author, Sandra Wachter, a researcher in data ethics at the University of Oxford in the United Kingdom and the Alan Turing Institute.

AI needs to answer "Why?"
 

As artificial intelligence (AI) becomes more sophisticated, it also becomes more opaque. Machine-learning algorithms can grind through massive amounts of data, generating predictions and making decisions without the ability to explain to humans what it’s doing. In matters of consequence—from hiring decisions to criminal sentencing—should we require justifications? A commentary published today in Science Robotics discusses regulatory efforts to make AI more transparent, explainable, and accountable. Science spoke with the article’s primary author, Sandra Wachter, a researcher in data ethics at the University of Oxford in the United Kingdom and the Alan Turing Institute.

AI needs to answer "Why?"
 

 

FINGER POINTING: When artificial intelligence botches your medical diagnosis, who’s to blame?

This inability arises from the opacity of AI systems, which—as a side effect of how machine-learning algorithms work—operate as black boxes. It’s impossible to understand why an AI has made the decision it has, merely that it has done so based upon the information it’s been fed. Even if it were possible for a technically literate doctor to inspect the process, many AI algorithms are unavailable for review, as they are treated as protected proprietary information. Further still, the data used to train the algorithms is often similarly protected or otherwise publicly unavailable for privacy reasons. This will likely be complicated further as doctors come to rely on AI more and more and it becomes less common to challenge an algorithm’s result.

AI needs to answer "Why?" Collaborative thought partner
 

This inability arises from the opacity of AI systems, which—as a side effect of how machine-learning algorithms work—operate as black boxes. It’s impossible to understand why an AI has made the decision it has, merely that it has done so based upon the information it’s been fed. Even if it were possible for a technically literate doctor to inspect the process, many AI algorithms are unavailable for review, as they are treated as protected proprietary information. Further still, the data used to train the algorithms is often similarly protected or otherwise publicly unavailable for privacy reasons. This will likely be complicated further as doctors come to rely on AI more and more and it becomes less common to challenge an algorithm’s result.

AI needs to answer "Why?" Collaborative thought partner
 

 

Dave Ferrucci on The Future of Artificial Intelligence

Dave Ferrucci speaks on the Future of AI at Bloomberg's The Year Ahead 2018 Conference

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AI needs to answer "Why?" Language understanding
Bloomberg
 

Dave Ferrucci speaks on the Future of AI at Bloomberg's The Year Ahead 2018 Conference

Watch Video

AI needs to answer "Why?" Language understanding
Bloomberg
 

 

Can A.I. Be Taught to Explain Itself?

As machine learning becomes more powerful, the field’s researchers increasingly find themselves unable to account for what their algorithms know — or how they know it.

Specfically asking, "How was the computer doing what it did? What was it seeing that humans could not?"

AI needs to answer "Why?"
New York Times Magazine
 

As machine learning becomes more powerful, the field’s researchers increasingly find themselves unable to account for what their algorithms know — or how they know it.

Specfically asking, "How was the computer doing what it did? What was it seeing that humans could not?"

AI needs to answer "Why?"
New York Times Magazine
 

 

How to Get Your Mind to Read

This article support's Elemental Cognition's belief that real comprehension is based on natural learning -- knowledge acquisition following a shared understanding.   Here, the author states that current education practices show that reading comprehension is misunderstood. It’s treated like a general skill that can be applied with equal success to all texts. Rather, comprehension is intimately intertwined with knowledge.

Reading comprehension
New York Times
 

This article support's Elemental Cognition's belief that real comprehension is based on natural learning -- knowledge acquisition following a shared understanding.   Here, the author states that current education practices show that reading comprehension is misunderstood. It’s treated like a general skill that can be applied with equal success to all texts. Rather, comprehension is intimately intertwined with knowledge.

Reading comprehension
New York Times
 

 

The impossibility of intelligence explosion

Elemental Cognition agrees with the author's arguments that the notion of an intelligence explosion comes from a profound misunderstanding of both the nature of intelligence and the behavior of recursively self-augmenting systems and that intelligence is fundamentally situational.

Intelligence
Medium
 

Elemental Cognition agrees with the author's arguments that the notion of an intelligence explosion comes from a profound misunderstanding of both the nature of intelligence and the behavior of recursively self-augmenting systems and that intelligence is fundamentally situational.

Intelligence
Medium
 

 

Why we are in danger of overestimating AI

Today’s AI is not true AI, it is simply deep learning, which, Elemental Cognition CEO Dr. Ferrucci notes, has predictive value but does not understand the way people do. This article discusses insights from Dr. Ferrucci and other AI experts on the need to remain analytical and skeptical about the current state and limitations of AI. Like Dr. Ferrucci, the other experts agree that AI research must go beyond deep learning and make progress in other areas of learning in order to eventually reach a level of understanding on par with humans.

Language understanding
 

Today’s AI is not true AI, it is simply deep learning, which, Elemental Cognition CEO Dr. Ferrucci notes, has predictive value but does not understand the way people do. This article discusses insights from Dr. Ferrucci and other AI experts on the need to remain analytical and skeptical about the current state and limitations of AI. Like Dr. Ferrucci, the other experts agree that AI research must go beyond deep learning and make progress in other areas of learning in order to eventually reach a level of understanding on par with humans.

Language understanding
 

 

Amazon Alexa and Google Home fall short of real conversation

While voice assistants exemplify exciting advances in speech recognition, they do not actually understand language. This article shares differing viewpoints from several AI experts (including Dr. Ferrucci) and startups about language processing and understanding. It notes that true language understanding will likely be the result of combining many different cognitive traits, and acknowledges Elemental Cognition’s mission to build a system that does that by constantly asking questions and refining its understanding of the world.

Language understanding
 

While voice assistants exemplify exciting advances in speech recognition, they do not actually understand language. This article shares differing viewpoints from several AI experts (including Dr. Ferrucci) and startups about language processing and understanding. It notes that true language understanding will likely be the result of combining many different cognitive traits, and acknowledges Elemental Cognition’s mission to build a system that does that by constantly asking questions and refining its understanding of the world.

Language understanding
 

 

Why this company will help change the future of artificial intelligence

"Elemental Cognition's technology seems to be aimed at the problem of going beyond the simple recognition/response models that dominate the A.I. landscape right now. I suspect Ferrucci is building out a technology that will combine outputs of deep learning and other machine-learning systems with the ability to draw inferences from, reason about and support decisions using the facts that they generate."

AI needs to answer "Why?" Language understanding
 

"Elemental Cognition's technology seems to be aimed at the problem of going beyond the simple recognition/response models that dominate the A.I. landscape right now. I suspect Ferrucci is building out a technology that will combine outputs of deep learning and other machine-learning systems with the ability to draw inferences from, reason about and support decisions using the facts that they generate."

AI needs to answer "Why?" Language understanding
 

 

A.I. Is Harder Than You Think

The crux of the problem is that the field of artificial intelligence has not come to grips with the infinite complexity of language. Just as you can make infinitely many arithmetic equations by combining a few mathematical symbols and following a small set of rules, you can make infinitely many sentences by combining a modest set of words and a modest set of rules. A genuine, human-level A.I. will need to be able to cope with all of those possible sentences, not just a small fragment of them.

 

The crux of the problem is that the field of artificial intelligence has not come to grips with the infinite complexity of language. Just as you can make infinitely many arithmetic equations by combining a few mathematical symbols and following a small set of rules, you can make infinitely many sentences by combining a modest set of words and a modest set of rules. A genuine, human-level A.I. will need to be able to cope with all of those possible sentences, not just a small fragment of them.