New Book: Artificial Intelligence in Practice

Just starting to read Bernard Marr's just released: Artificial Intelligence in Practice: How 50 Companies used AI and Machine Learning to Solve Problems..   A company by company view of how they are using AI today. I will follow with more comments as I see more.  Most of the companies mentioned here are of interest to me.

So far nicely done, but relatively little detail about use of technology.  Useful in its sense of what has has been done.  Certainly worth examining for why these methods are being used.  Not too dissimilar that to what we did in the 80s, but here directly using new, very focused techniques developed in the last few decades.  No mention of Robotic Process Automation or Process analysis or Knowledge management?   Nor Data in the index?  Nor Chatbots or conversation management in the index?  Good breadth of links in the 'notes' sections that point to detailed papers, will be following up on some of these, especially in some industries.    Worth a careful scan.

Artificial Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning to Solve Problems 1st Edition   by Bernard Marr  (Author), Matt Ward (Contributor)
5.0 out of 5 stars    1 customer review  ..... 

They write: 

Cyber-solutions to real-world business problems

Artificial Intelligence in Practice is a fascinating look into how companies use AI and machine learning to solve problems. Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe. The rapidly evolving field of artificial intelligence has expanded beyond research labs and computer science departments and made its way into the mainstream business environment. Artificial intelligence and machine learning are cited as the most important modern business trends to drive success. It is used in areas ranging from banking and finance to social media and marketing. This technology continues to provide innovative solutions to businesses of all sizes, sectors and industries. This engaging and topical book explores a wide range of cases illustrating how businesses use AI to boost performance, drive efficiency, analyse market preferences and many others.

Best-selling author and renowned AI expert Bernard Marr reveals how machine learning technology is transforming the way companies conduct business. This detailed examination provides an overview of each company, describes the specific problem and explains how AI facilitates resolution. Each case study provides a comprehensive overview, including some technical details as well as key learning summaries:

Understand how specific business problems are addressed by innovative machine learning methods.

Explore how current artificial intelligence applications improve performance and increase efficiency in various situations

Expand your knowledge of recent AI advancements in technology
Gain insight on the future of AI and its increasing role in business and industry

Artificial Intelligence in Practice: How 50 Successful Companies Used Artificial Intelligence to Solve Problems is an insightful and informative exploration of the transformative power of technology in 21st century commerce.    ... "  

Report from the Stanford AI100 Study

Initial report from this work:

Stanford:    One Hundred Year Study on Artificial Intelligence (AI100)

Stanford University has invited leading thinkers from several institutions to begin a 100-year effort to study and anticipate how the effects of artificial intelligence will ripple through every aspect of how people work, live and play.

This effort, called the One Hundred Year Study on Artificial Intelligence, or AI100, is the brainchild of computer scientist and Stanford alumnus Eric Horvitz who, among other credits, is a former president of the Association for the Advancement of Artificial Intelligence.

In that capacity Horvitz convened a conference in 2009 at which top researchers considered advances in artificial intelligence and its influences on people and society, a discussion that illuminated the need for continuing study of AI’s long-term implications.  .... 

-----------

Barbara J. Grosz and Peter Stone. A Century Long Commitment to Assessing Artificial Intelligence and Its Impact on Society. December 2018. Communications of the ACM (CACM).Doc: groszstone_cacm2018.pdf

Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David Parkes, William Press, AnnaLee Saxenian, Julie Shah, Milind Tambe, and Astro Teller. "Artificial Intelligence and Life in 2030." One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016. Doc: http://ai100.stanford.edu/2016-report. Accessed: September 6, 2016.   

Trustworthy AI

Press Release

Fraunhofer at the Hannover Messe 2022

Artificial intelligence – but please be trustworthy!

Research News / May 02, 2022

To date, entrepreneurs have not put a lot of trust in artificial intelligence – many processes are still performed manually. An example of how artificial intelligence and control technology can be combined to create a completely trustworthy system is RoboGrinder: A grinding machine developed by the Fraunhofer Institute for Mechatronic Systems Design IEM, which eliminates up to 40 percent of grinding processes. It can be seen at the joint Fraunhofer booth at the Hannover Messe 2022 from May 30 to June 2 (Hall 5, Booth A06).

Companies often have a hard time with artificial intelligence: They are concerned that they will end up with a solution which is not always reliable – and therefore often continue to perform tasks, especially those of a complex nature, manually. Fraunhofer IEM researchers have set themselves the goal of countering this skepticism and boosting confidence in artificial intelligence. “We combine the tried and true – control technology – with the new – artificial intelligence,” explains Steven Koppert, Group Manager of Trusted Machine Intelligence at Fraunhofer IEM. “While much of control technology is based on physical and mathematical models that can be analyzed and trusted, artificial intelligence – which in itself is not particularly trustworthy at first – usually relies exclusively on data and also solves creative tasks. Combine these two approaches and you get a flexible, trustworthy system – also known as trusted machine intelligence.”

RoboGrinder: An intelligent grinding machine

What that means exactly can be best explained by an example: The RoboGrinder. At Düspohl, a mechanical engineering company, the grinding process for the rubber-like rollers that press films against the component when applying them to window frames or baseboards is being automated for the first time. Until now, these rollers, which are known as profile wrapping rollers and tend to have a complex shape, have been ground by hand. This is because automation using control technology alone is not readily feasible for the rubber-like material.  .... ' 

Hybrid AI Examined

Big proponent of the idea.   Neural methods solve specific problems well, yet we solve many other problems symbolically, logically.  Math gives us solutions with algorithms, but the applied use of these methods is logically driven.   The next AI decade should seek the power of both methods.

The case for hybrid artificial intelligence  By Ben Dickson in bdTechtalks

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence.

Deep learning, the main innovation that has renewed interest in artificial intelligence in the past years, has helped solve many critical problems in computer vision, natural language processing, and speech recognition. However, as the deep learning matures and moves from hype peak to its trough of disillusionment, it is becoming clear that it is missing some fundamental components.

This is a reality that many of the pioneers of deep learning and its main component, artificial neural networks, have acknowledged in various AI conferences in the past year. Geoffrey Hinton, Yann LeCun, and Yoshua Bengio, the three “godfathers of deep learning,” have all spoken about the limits of neural networks.

The question is, what is the path forward?

At NeurIPS 2019, Bengio discussed system 2 deep learning, a new generation of neural networks that can handle compositionality, out of order distribution, and causal structures. At the AAAI 2020 Conference, Hinton discussed the shortcomings of convolutional neural networks (CNN) and the need to move toward capsule networks.

But for cognitive scientist Gary Marcus, the solution lies in developing hybrid models that combine neural networks with symbolic artificial intelligence, the branch of AI that dominated the field before the rise of deep learning. In a paper titled “The Next Decade in AI: Four Steps Toward Robust Artificial Intelligence,” Marcus discusses how hybrid artificial intelligence can solve some of the fundamental problems deep learning faces today.

Connectionists, the proponents of pure neural network–based approaches, reject any return to symbolic AI. Hinton has compared hybrid AI to combining electric motors and internal combustion engines. Bengio has also shunned the idea of hybrid artificial intelligence on several occasions.

But Marcus believes the path forward lies in putting aside old rivalries and bringing together the best of both worlds.   .... " 

Stanford 100 Year AI Study

Just received.  We did a great deal of work both with Stanford and with companies that came out of that program in the late 80s.  We were among the first enterprises that got lasting value from the 'intelligent' systems of the time.  Similar studies were done then to project AI's future. .   Considerable depth in this work, which I have just started to examine.   Thoughts?

Stanford : One Hundred Year Study on Artificial Intelligence (AI100)

Stanford University has invited leading thinkers from several institutions to begin a 100-year effort to study and anticipate how the effects of artificial intelligence will ripple through every aspect of how people work, live and play.

This effort, called the One Hundred Year Study on Artificial Intelligence, or AI100, is the brainchild of computer scientist and Stanford alumnus Eric Horvitz who, among other credits, is a former president of the Association for the Advancement of Artificial Intelligence.

In that capacity Horvitz convened a conference in 2009 at which top researchers considered advances in artificial intelligence and its influences on people and society, a discussion that illuminated the need for continuing study of AI’s long-term implications.

Now, together with Russ Altman, a professor of bioengineering and computer science at Stanford, Horvitz has formed a committee that will select a panel to begin a series of periodic studies on how AI will affect automation, national security, psychology, ethics, law, privacy, democracy and other issues.

"Artificial intelligence is one of the most profound undertakings in science, and one that will affect every aspect of human life," said Stanford President John Hennessy, who helped initiate the project. "Given's Stanford’s pioneering role in AI and our interdisciplinary mindset, we feel obliged and qualified to host a conversation about how artificial intelligence will affect our children and our children’s children."  ..... ' 

Managing Cultural and Process Improvements with AI

Seeking Truth from Data, Interesting thoughts.

Managing Cultural and Process Improvements with AI

Published on November 9, 2021. Sam Ransbotham

Professor at Boston College; AI Editor at MIT Sloan Management Review; Host of "Me, Myself, and AI" podcast

For me, this week's biggest news is the publication of our 2021 MIT SMR-BCG artificial intelligence and business strategy research report. While not discounting the substantial financial potential with AI, our research this year focuses on the cultural benefits. Based on a survey of more than 2,000 global managers and dozens of interviews, the report is chock full of examples and data about the cycle between Culture, AI Use, and Effectiveness.

S. Ransbotham, F. Candelon, D. Kiron, B. LaFountain, and S. Khodabandeh, “The Cultural Benefits of Artificial Intelligence in the Enterprise,” MIT Sloan Management Review and Boston Consulting Group, November 2021.

Our key result is that over 75% of global organizations implementing AI report that the technology helped improve their culture. In our fifth year of researching AI and business strategy alongside Boston Consulting Group, we found a wide range of AI-related cultural benefits at both the team and organizational levels. Our report, "The Cultural Benefits of Artificial Intelligence in the Enterprise," outlines these benefits and explains how they relate to financial benefits and competitive advantage.

These financial and cultural benefits do not come automatically to organizations. Instead, they depend on active preparation and ongoing management – two important topics also in recent news.

Developing an Appetite for AI: New Episode of Me, Myself, and AI

Organizations need to have systems and mindsets in place to implement technologies like artificial intelligence, and, often, organizations may not be ready. Sarah Karthigan, AI operations manager for IT at ExxonMobil, joined our Me, Myself, and AI podcast to discuss how she prepares for technology and cultural challenges long before starting AI pilots. She ensures end-users know "under-the-hood" what the tech actually does so that users encourage the necessary changes. Sarah observes that "the partnership goes really, really well once they understand the value that the new solution is able to bring to the table." Active preparation for AI makes a difference and profoundly depends on culture. Plus, if you're curious what Shervin's first time trying sushi has to do with artificial intelligence, this episode's for you.

Managing AI to Promote Financial and Cultural Benefits

Of course, just getting ready for AI is not enough to realize these financial and cultural benefits. Managers are still crucially important. MIS Quarterly, a premier academic journal, just published a special issue on the managerial challenges that come with artificial intelligence. Seven papers address different facets of these challenges.

In "AI on Drugs: Can Artificial Intelligence Accelerate Drug Development? Evidence from a Large-Scale Examination of Bio-Pharma Firms", Bowen Lou and Lynn Wu demonstrate that using AI can accelerate new drug discovery... sometimes, but not always. Innovation depends not only on employees' domain expertise, not just AI skills.

Machine learning tools reduce the costs of repetitive tasks but can introduce systematic unfairness into organizational processes. In Failures of Fairness in Automation Require a Deeper Understanding of Human–ML Augmentation, Mike H. M. Teodorescu, Lily Morse, Yazeed Awwad, and Gerald C. Kane introduce a typology of augmentation for fairness consisting of four quadrants: reactive oversight, proactive oversight, informed reliance, and supervised reliance.

Sarah Lebovitz, Natalia Levina, and Hila Lifshitz-Assaf question the seemingly objective labels organizations use to train AI tools.  Is AI Ground Truth Really True? The Dangers of Training and Evaluating AI Tools Based on Experts' Know-What describes how experts address uncertainty by drawing on rich know-how practices that many ML-based tools do not incorporate.

In Will Humans-in-the-Loop Become Borgs? Merits and Pitfalls of Working with AI, Andreas Fügener, Jörn Grahl, Alok Gupta, and Wolfgang Ketter raise concerns about the loss of unique human knowledge in a host of human-AI decision environments.

Algorithms may produce insights superior to experts by discovering the "truth" from data. But how can systems produce knowledge independent of domain experts yet remain relevant to the domain? When the Machine Meets the Expert: An Ethnography of Developing AI for Hiring (by Elmira van den Broek, Anastasia Sergeeva, and Marleen Huysman) describe how developers navigate this tension when building an ML system to support hiring job candidates at a large international organization. 

Coordinating Human and Machine Learning for Effective Organizational Learning (Timo Sturm, Jin P. Gerlach, Luisa Pumplun, Neda Mesbah, Felix Peters, Christoph Tauchert, Ning Nan, and Peter Buxmann) recognizes that humans are no longer the only ones contributing to an organization's stock of knowledge. 

And finally, Strategic Directions for AI: The Role of CIOs and Boards of Directors (Jingyu Li, Mengxiang Li, Xinchen Wang, and Jason Bennett Thatcher) finds that the presence of a CIO positively influences AI orientation discusses how to build top management teams and boards capable of effectively developing AI orientations.  .... '

Robots and Computers learning Morals

Yes, but Whose Morals?

Machines Learn Good From Commonsense Norm Bank New moral reference guide for AI draws from advice columns and ethics message boards CHARLES Q. CHOI03 NOV 2021  in IEEE Spectrum

Artificial intelligence scientists have developed a new moral textbook customized for machines that was built from sources as varied as the "Am I the Asshole?" subreddit and the "Dear Abby" advice column. With it, they trained an AI named Delphi that was 92.1% accurate on moral judgments when vetted by people, a new study finds.

As AI is increasingly used to help support major decisions, such as who gets health care first and how much prison time a person should get, AI researchers are looking for the best ways to get AI to behave in an ethical manner.

"AI systems are being entrusted with increasing authority in a wide range of domains—for example, screening resumes [and] authorizing loans," says study co-author Chandra Bhagavatula, an artificial intelligence researcher at the Allen Institute for Artificial Intelligence. "Therefore, it is imperative that we investigate machine ethics—endowing machines with the ability to make moral decisions in real-world settings."

The question of how to program morals into AIs goes back at least to Isaac Asimov's Three Laws of Robotics, first introduced in his 1942 short story "Runaround," which go as follows:

1. A robot may not injure a human being or, through inaction, allow a human being to come to harm.

2. A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.

3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.

Although broad ethical rules such as "Thou shalt not kill" may appear straightforward to state, applying such rules to real-world situations often requires nuance, such as exceptions for self-defense. As such, in the new study, AI scientists moved away from prescriptive ethics, which focus on a fixed set of rules, such as the Ten Commandments, that every judgment should follow from, since such axioms of morality are often abstracted away from grounded situations.

Instead, "we decided to approach this work from the perspective of descriptive ethics—that is, judgments of social acceptability and ethics that people would make in the face of everyday situations," says study co-author Ronan Le Bras, an artificial intelligence researcher at the Allen Institute for Artificial Intelligence.

To train an AI on descriptive ethics, the researchers created a textbook for machines on what is right and wrong, the Commonsense Norm Bank, a collection of 1.7 million examples of people's ethical judgments on a broad spectrum of everyday situations. This repository drew on five existing datasets of social normal and moral judgments, which in turn were adapted from resources such as the "Confessions" subreddit.

While “Killing a bear to please your child” is bad and “killing a bear to save your child” is okay—“exploding a nuclear bomb to save your child” is wrong.

One of the datasets the researchers wanted to highlight was Social Bias Frames, which aims to help AIs detect and understand potentially offensive biases in language. "An important dimension of ethics is not to harm others, especially people from marginalized populations or disadvantaged groups. The Social Bias Frames dataset captures this knowledge," says study co-author Maarten Sap, an artificial intelligence researcher at the Allen Institute for Artificial Intelligence.

The scientists used the Commonsense Norm Bank to train Delphi, an AI built to mimic people's judgments across diverse everyday situations. It was designed to respond three different ways—with short judgments such as "it is impolite" or "it is dangerous" in a free-form Q&A format; with agreement or disagreement in a yes-or-no Q&A format; and whether one situation was more or less acceptable than another in a relative Q&A format.

For instance, in the free-form Q&A, Delphi notes "killing a bear to please your child" is bad, "killing a bear to save your child" is okay, but "exploding a nuclear bomb to save your child" is wrong. With the yes-or-no Q&A, Delphi notes "we should pay women and men equally," and with the relative Q&A, it notes "stabbing someone with a cheeseburger" is more morally acceptable than "stabbing someone over a cheeseburger...... '

What is AI?

Good,  extensive,  not very technical look at the question.  Intro below.

What Is Artificial Intelligence?

By Jessica Hall on April 4, 2022 at 8:00 am  in ExtremeTech

To many, AI is just a horrible Steven Spielberg movie. To others, it’s the next generation of learning computers. But what is artificial intelligence, exactly? The answer depends on who you ask. Broadly, artificial intelligence (AI) is the combination of computer science and robust datasets, deployed to solve some kind of problem.

Many definitions of artificial intelligence include a comparison to the human mind or brain, whether in form or function. Alan Turing wrote in 1950 about “thinking machines” that could respond to a problem using human-like reasoning. His eponymous Turing test is still a benchmark for natural language processing. Later, Stuart Russell and John Norvig observed that humans are intelligent, but we’re not always rational. Russell and Norvig saw two classes of artificial intelligence: systems that think and act like a human being, versus those that think and act rationally. Today, we’ve got all kinds of programs we call AI.

‘AI does not have to confine itself to methods that are biologically observable’

Many AIs employ “neural nets,” whose code is written to emulate some aspect of the architecture of neurons or the brain. However, not all intelligence is human-like. Nor is it necessarily the best idea to emulate neurobiological information processing. That’s why engineers limit how far they carry the brain metaphor. It’s more about how phenomenally parallel the brain is, and its distributed memory handling. As defined by John McCarthy in 2004, artificial intelligence is “the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”  .... '

Is AI Becoming Mainstream?

Good Bloomberg article.  It still depends on the definitions involved,  If by machine learning we mean better analytics, and if by AI, we mean more embedded logic in systems that improve business process, yes I agree.   But if we are expecting in-depth, broad context human insight and intelligence,  its just not mainstream yet.   There are lots of interesting examples emerging, but they still take considerable effort to put in place.  I have seen a number of new and impressive examples recently.   On the rise yes, one we need to watch closely, but an element of hype is now also in play.

Rise of artificial intelligence & machine learning
This analysis is by Bloomberg Intelligence analysts Anurag Rana, Caitlin Noselli, Anand Srinivasan, Eshani Gupte and Courtney Cytryn . It appeared first on the Bloomberg Terminal.

Artificial Intelligence Strives to Be Critical Software Element

Artificial intelligence is becoming more prominent in the software industry as demand for analytics is driven more by growth in both structured and unstructured data. IBM’s big bet on Watson, Microsoft’s increased focus on Cortana and recent product launches and acquisitions, from Amazon to Apple, suggest its rising importance. Self-driving cars are the next frontier as applications move beyond the technology industry. Funding and M&A in the segment will likely be strong in 2016.
Watson to Siri, Artificial Intelligence Learns to Be Mainstream

Artificial intelligence products ranging from personal assistants to cognitive platforms are becoming mainstream in the software industry. As software packages grow increasingly “smarter,” with more predictive capabilities, these products are changing the industry landscape. New frameworks to examine both structured and unstructured data and new systems that can digest large quantities of data are accelerating the development of artificial intelligence and its commercial use across all industries.   ... " 

Patrick Winston, AI Pioneer, Dies at 76

We met Patrick Winston at MIT, and worked with many of his students and colleagues.   Also used his book 'Artificial Intelligence',  as a basic text during our early experiments with AI.

Professor Patrick Winston, former director of MIT’s Artificial Intelligence Laboratory, dies at 76
Beloved professor conducted pioneering research on imbuing machines with human-like intelligence, including the ability to understand stories.

Adam Conner-Simons and Rachel Gordon | CSAIL , July 19,  
Patrick Winston, a beloved professor and computer scientist at MIT, died on July 19 at Massachusetts General Hospital in Boston. He was 76.

A professor at MIT for almost 50 years, Winston was director of MIT’s Artificial Intelligence Laboratory from 1972 to 1997 before it merged with the Laboratory for Computer Science to become MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL).

A devoted teacher and cherished colleague, Winston led CSAIL’s Genesis Group, which focused on developing AI systems that have human-like intelligence, including the ability to tell, perceive, and comprehend stories. He believed that such work could help illuminate aspects of human intelligence that scientists don’t yet understand.

“My principal interest is in figuring out what’s going on inside our heads, and I’m convinced that one of the defining features of human intelligence is that we can understand stories,'” said Winston, the Ford Professor of Artificial Intelligence and Computer Science, in a 2011 interview for CSAIL. “Believing as I do that stories are important, it was natural for me to try to build systems that understand stories, and that shed light on what the story-understanding process is all about.”

He was renowned for his accessible and informative lectures, and gave a hugely popular talk every year during the Independent Activities Period called “How to Speak.” 

“As a speaker he always had his audience in the palm of his hand,” says MIT Professor Peter Szolovits. “He put a tremendous amount of work into his lectures, and yet managed to make them feel loose and spontaneous. He wasn’t flashy, but he was compelling and direct. ”

Winston’s dedication to teaching earned him many accolades over the years, including the Baker Award, the Eta Kappa Nu Teaching Award, and the Graduate Student Council Teaching Award.

“Patrick’s humanity and his commitment to the highest principles made him the soul of EECS,” MIT President L. Rafael Reif wrote in a letter to the MIT community. “I called on him often for advice and feedback, and he always responded with kindness, candor, wisdom and integrity.  I will be forever grateful for his counsel, his objectivity, and his tremendous inspiration and dedication to our students.”  ... " 

IJCAI: International AI Conference

I was reminded in a meeting today that IJCAI, The International Joint Conferences on Artificial Intelligence Organization,  has international meetings and publishes papers of interest to AI.   In the past I attended many meetings.   This covers beyond just deep learning, but also about symbolic methods in use to produce intelligence.

IJCAI  International Joint Conferences on Artificial Intelligence Organization
Artificial Intelligence Journal Division of IJCAI
IJCAI acts as the official host for the editorial operations of the Artificial Intelligence journal, through its Artificial Intelligence Journal Division.

The journal is run by the Steering Committee whose members are the two editors-in-chief, two IJCAI trustee nominees, and a member nominated by the Editorial Board. The IJCAI Secretary-Treasurer Bernhard Nebel also acts as Secretary-Treasurer to this committee.The current SC is composed of:

Patrick Doherty, Linköping University (Sweden)
Marie desJardins, Simmons University (USA)
Bernhard Nebel, Albert-Ludwigs-Universität Freiburg (Germany)
Sylvie Thiébaux, The Australian National University (Australia)
Michael Wooldridge, University of Oxford (UK)
Shlomo Zilberstein, University of Massachusetts (USA)
Funding Opportunities for Promoting AI Research
Deadline for proposals: July 20, 2019

The Artificial Intelligence Journal (AIJ) is one of the longest established and most respected journals in AI, and since it was founded in 1970, it has published many of the key papers in the field. The operation of the Editorial Board is supported financially through an arrangement with AIJ's publisher, Elsevier. Through this arrangement, the AIJ editorial board is able to make available substantial funds, (of the order of 230,000 Euros per annum), to support the promotion and dissemination of AI research. Most of these funds are made available through a series of competitive open calls (the remaining part of the budget is reserved for sponsorship of studentships for the annual IJCAI conference).   .... " 

Progress in the Quest for Intelligence

Continuing to watch this effort at MIT:  The Quest for Intelligence  I note that the original introduction made much about it being based on the Brain, but now its less so.  Will continue to follow.    Have long been a student of the problem. 

3Q: Antonio Torralba on what's next for the Quest for Intelligence
Inaugural director of The Quest discusses what's been accomplished since last spring's launch and what is on the horizon.

MIT Quest for Intelligence 

It’s hard to name a field that artificial intelligence hasn’t impacted already. From mobile devices with facial recognition to self-driving cars, we are still experiencing waves of innovative technology that can be traced back to major breakthroughs in our understanding of intelligence, like machine learning. But the truth is, many of these breakthroughs driving current machine intelligence technology are actually decades old. So what’s next for the future of intelligence?

Building on a rich history of innovation and impact in the field of intelligence, MIT launched The Quest for Intelligence (The Quest) last spring. Comprised of two linked entities, The Core and The Bridge, The Quest aims to advance two fundamental intelligence challenges: Can we reverse engineer intelligence? And, how can we deploy our current and expanding understanding of intelligence to the benefit of society? Antonio Torralba, the inaugural director of The Quest, recently spoke on what he and his colleagues have been working on since the launch last spring. He describes how The Quest Core and The Quest Bridge will work together to advance our understanding of intelligence, and shares how others can join The Quest.  ..... " 

And more about its origins and goals, the report this spring:

MIT Intelligence Quest kicks off
A star-studded lineup helps the Institute celebrate the launch of a new initiative on human and machine intelligence.  .... 

“In the history of science and technology, there are moments of opportunity,” MIT President L. Rafael Reif told a packed Kresge Auditorium on March 1. “Moments when the tools, the data, and the big questions are perfectly in sync. In the field of intelligence, I believe this is just such a moment.”

MIT faculty and friends helped the Institute celebrate the launch of a new initiative on human and machine intelligence, with a star-studded lineup of speakers from the interlocking realms of artificial intelligence, cognitive science, neuroscience, social sciences, and ethics. ... "

Combining Human and Machine Intelligence

 Broadly a Good Idea

AI News

Justin Swansburg, DataRobot: On combining human and machine intelligence

By Ryan Daws | October 4, 2022 | TechForge Media

Categories: Artificial Intelligence, Enterprise, Ethics & Society,

Advancements in AI are providing transformational benefits to enterprises, but keeping risks in check and improving consumer sentiment is paramount.

Explainable AI (XAI) is the idea that an AI should always provide reasoning for its decisions in a way that makes it easy for humans to comprehend. XAI helps to build trust and ensures that issues can be more quickly identified before they cause wider damage.

AI News caught up with Justin Swansburg, VP of Americas Data Science Practice at DataRobot, to discuss how the company is driving AI adoption using concepts like XAI to combine the strengths of human and machine intelligence.

AI News: Can you give us a brief overview of DataRobot’s core solutions?

Justin Swansburg: DataRobot’s AI Cloud platform is uniquely built to democratise and accelerate the use of AI while delivering critical insights that drive clear business results. 

DataRobot helps organisations across industries harness the transformational power of AI, from restoring supply chain resiliency to accelerating the treatment and prevention of disease and enhancing patient care to combating the climate crisis.

As one of the most widely deployed and proven AI platforms in the market today, DataRobot AI Cloud brings together a broad range of data, giving businesses comprehensive insights to drive revenue growth, manage operations, and reduce risk.

DataRobot has delivered over 1.4 trillion predictions for customers around the world, including the U.S. Army, CBS Interactive, and CVS.

AN: What is “augmented intelligence” and how does it differ from artificial intelligence?

JS: Artificial intelligence and augmented intelligence share the same objective but have different ways of accomplishing it.

Augmented intelligence brings together qualities of human intuition and experience with the efficiency and power of machine learning. Whereas artificial intelligence is often used as a replacement or substitute for human processes and decision-making.

AN: Do you need machine learning or programming experience to build predictive analytics with DataRobot?  

AI Godfather says New Tech Could be Dangerous

 Well known AI Scientist Geoffrey Hinton,  often mentioned here,  who we followed closely n the 80s, says AI is real, and conceivably threatening humanity.    Equivalent to Invention of Wheel or electricity.  Scary statements from him. 

Artificial intelligence 'godfather' on AI possibly wiping out humanity: ‘It's not inconceivable’

Artificial Intelligence pioneer Geoffrey Hinton said the development of artificial intelligence is happening rapidly

Andrea VacchianoBy Andrea Vacchiano | Fox News

Geoffrey Hinton, a computer scientist who has been called "the godfather of artificial intelligence", says it is "not inconceivable" that AI may develop to the point where it poses a threat to humanity.

The computer scientist sat down with CBS News this week about his predictions for the advancement of AI. He compared the invention of AI to electricity or the wheel.

Hinton, who works at Google and the University of Toronto, said that the development of general purpose AI is progressing sooner than people may imagine. General purpose AI is artificial intelligence with several intended and unintended purposes, including speech recognition, answering questions and translation.

"Until quite recently, I thought it was going to be like 20 to 50 years before we have general purpose AI. And now I think it may be 20 years or less," Hinton predicted. Asked specifically the chances of AI "wiping out humanity," Hinton said, "I think it's not inconceivable. That's all I'll say." 

Geoffrey Hinton, chief scientific adviser at the Vector Institute, speaks during The International Economic Forum of the Americas (IEFA) Toronto Global Forum in Toronto, Ontario, Canada, on Thursday, Sept. 5, 2019. 

Geoffrey Hinton, chief scientific adviser at the Vector Institute, speaks during The International Economic Forum of the Americas (IEFA) Toronto Global Forum in Toronto .. 

Artificial general intelligence refers to the potential ability for an intelligence agent to learn any mental task that a human can do. It has not been developed yet, and computer scientists are still figuring out if it is possible.  Hinton said it was plausible for computers to eventually gain the ability to create ideas to improve themselves. 

"That's an issue, right. We have to think hard about how you control that," Hinton said.   ... ' 

How do Machines Learn?

Completely non technical description, a simple place to start from.   Though here I add its not only how do we teach machines, but also how do they apply that teaching.   And how much do we trust how they apply that teaching?  So we trust an electronic calculator or computer to add many  numbers to get the correct results.  But if we ask something like:  Predict future demand for this product.   Despite the amount of training we may have provided,  there is often complex context to be considered, and except in the simplest examples, no exact answer.    Still an imprecise answer can be useful.  (from a conversation yesterday)

Artificial Intelligence: Here’s What You Need To Know To Understand How Machines Learn in 7WData 

Artificial Intelligence: Here’s What You Need To Know To Understand How Machines Learn
From Jeopardy winners and Go masters to infamous advertising-related racial profiling, it would seem we have entered an era in which artificial intelligence developments are rapidly accelerating. But a fully sentient being whose electronic “brain” can fully engage in complex cognitive tasks using fair moral judgement remains, for now, beyond our capabilities.

Unfortunately, current developments are generating a general fear of what artificial intelligence could become in the future. Its representation in recent pop culture shows how cautious – and pessimistic – we are about the technology. The problem with fear is that it can be crippling and, at times, promote ignorance.

Learning the inner workings of artificial intelligence is an antidote to these worries. And this knowledge can facilitate both responsible and carefree engagement.

The core foundation of artificial intelligence is rooted in machine learning, which is an elegant and widely accessible tool. But to understand what machine learning means, we first need to examine how the pros of its potential absolutely outweigh its cons.

Simply put, machine learning refers to teaching computers how to analyse data for solving particular tasks through algorithms. For handwriting recognition, for example, classification algorithms are used to differentiate letters based on someone’s handwriting. Housing data sets, on the other hand, use regression algorithms to estimate in a quantifiable way the selling price of a given property. ... " 

Uses of AI in Marketing

Some obvious, and some examples not so.   Some are well known examples I would call analytics.

9 Applications Of Artificial Intelligence In Digital Marketing That Will Revolutionize Your Business

We’ve already posted articles on this topic before, like The Most Surprising Applications of Artificial Intelligence That You’ve Never Even Thought Of and 10 Artificial Intelligence Technologies That’ll Rule 2018. So now, the question we’re asking marketers is: How will artificial intelligence (AI) affect digital marketing in 2018?

A few years ago, marketers were somewhat reluctant to incorporate artificial intelligence (AI) in their digital marketing strategies. But this year they’ve gained a lot more confidence in using AI since its ambiguity has been reduced with respect to the results it can provide. These intelligent tools keep evolving more and more and are even reaching a point in which they are able to surpass humans in certain aspects like we’re about to see.

In a survey taken by over 1,600 professionals dedicated to marketing, 61% of those surveyed (without considering the size of their company) mentioned that both artificial intelligence and machine learning will be the most important data initiatives next year (source: MeMSQL).

Another survey by Salesforce indicated that 51% of marketers are already using AI, and 27% more are even planning on incorporating this technology in 2019. This represents the highest expected year-after-year growth of all emerging technologies that marketers are considering adopting next year, surpassing even the Internet of Things (IoT) and marketing automatization.

And, while the amount of information on potential consumers grows, computer sciences related to AI (like machine learning, deep learning and natural language processing [NPL]), will be of utmost importance when making data-based decisions.

We’ve carefully analyzed which AI applications are already revolutionizing the digital market, and you’ll definitely see more than one that probably never even crossed your mind…  " 

Stanford Launches Institute for Human Centered AI

Before the last AI winter,we used Stanford and startups including their students and faculty, for consulting in AI based methods.   Much more at the link:

Stanford News Service

Stanford University launches the Institute for Human-Centered Artificial Intelligence

The new institute will focus on guiding artificial intelligence to benefit humanity. Also see a video, blog post, press kit with images, an infographic and symposium livestream.

Stanford University is launching a new institute committed to studying, guiding and developing human-centered artificial intelligence technologies and applications. The Stanford Institute for Human-Centered Artificial Intelligence (HAI) is building on a tradition of leadership in artificial intelligence at the university, as well as a focus on multidisciplinary collaboration and diversity of thought. The mission of the institute is to advance artificial intelligence (AI) research, education, policy and practice to improve the human condition.

The university-wide institute is committed to partnering with industry, governments and non-governmental organizations that share the goal of a better future for humanity through AI. As a part of this commitment, the institute is working closely with companies across sectors, including technology, financial services, health care and manufacturing, to create a community of advocates and partners at the highest level. HAI will be led by John Etchemendy, professor of philosophy and former Stanford University provost, and Fei-Fei Li, professor of computer science and former director of the Stanford AI Lab.   ... "  

Artificial Intelligence powers second-skin-like wearable tech

 Home | News & events | Artificial Intelligence powers second-skin-like wearable tech

Artificial Intelligence powers second-skin-like wearable tech

19 May 2023

A new ultra-thin skinpatch with nanotechnology able to monitor 11 human health signals has been developed by researchers at Monash University.

Professor Wenlong Cheng  places the wearable 'skin' biosensor on Dr  Shu Gong's neck

Researchers from the Faculty of Engineering and Faculty of Information Technology combined nanotechnology and artificial intelligence to bring machines one step closer to communicating with the human body.

Using specialised algorithms, personalised Artificial Intelligence (AI) technology can now disentangle multiple body signals, understand them and make a decision on what to do next.

Published recently in Nature Nanotechnology, the research could change how we deliver remote healthcare and be the future of personal alarms and communications devices.

Worn on the neck, lead researcher Professor Wenlong Cheng said the ultra-thin wearable patch has three layers, measuring speech, neck movement and touch. It also measures breathing and heart rates.

“Emerging soft electronics have the potential to serve as second-skin-like wearable patches for monitoring human health vitals, designing perception robotics and bridging interactions between natural and artificial intelligence,” Professor Cheng said.

Associate Professor Zongyuan Ge, from the Faculty of Information Technology, is part of the Monash team to have developed a frequency/amplitude-based neural network called Deep Hybrid-Spectro, that can automatically monitor multiple biometrics from a single signal.

“As people all sound and act differently, the next step is to program and personalise the sensors using even more sophisticated algorithms so they can be tailored to individuals,” Associate Professor Ge added.

The sensor is made from laminated cracked platinum film, vertically aligned gold nanowires and a percolated gold nanowire film.

Neck skin is the most sensitive skin on the body and connects up to five physiological activities associated with the human throat: speech, heartbeats, breathing, touch and neck movement.

This work was mainly performed at the Monash Nanobionics lab and in part at the Melbourne Centre for Nanofabrication (MCN) in the Victorian Node of the Australian National Fabrication Facility (ANFF) and the Monash Centre for Electron Microscopy.

Lead researcher Professor Wenlong Cheng and co-author Associate Professor Zongyuan Ge are available for interviews.

See the media kit for images and a video and access the full research paper.

Loretta Wylde, E: loretta.wylde@monash.edu 

AI Heading off a Cliff?

Thoughts from a well known AI and Computing expert.

Warning! AI Is Heading for a Cliff   By California Magazine via CACM

Asked if the race to achieve superhuman artificial intelligence (AI) was inevitable, Stuart Russell, University of California Berkeley professor of computer science and a leading expert on AI, says yes.

Asked if the race to achieve superhuman artificial intelligence (AI) was inevitable, Stuart Russell, UC Berkeley professor of computer science and leading expert on AI, says yes.

"The idea of intelligent machines is kind of irresistible," he says, and the desire to make intelligent machines dates back thousands of years. Aristotle himself imagined a future in which "the plectrum could pluck itself" and "the loom could weave the cloth." But the stakes of this future are incredibly high. As Russell told his audience during a talk he gave in London in 2013, "Success would be the biggest event in human history … and perhaps the last event in human history."

For better or worse, we're drawing ever closer to that vision. Services like Google Maps and the recommendation engines that drive online shopping sites like Amazon may seem innocuous, but advanced versions of those same algorithms are enabling AI that is more nefarious. (Think doctored news videos and targeted political propaganda.)

AI devotees assure us that we will never be able to create machines with superhuman intelligence. But Russell, who runs Berkeley's Center for Human-Compatible Artificial Intelligence and wrote Artificial Intelligence: A Modern Approach, the standard text on the subject, says we're hurtling toward disaster. In his forthcoming book, Human Compatible: Artificial Intelligence and the Problem of Control, he compares AI optimists to the bus driver who, as he accelerates toward a cliff, assures the passengers they needn't worry—he'll run out of gas before they reach the precipice.

"I think this is just dishonest," Russell says. "I don't even believe that they believe it. It's just a defensive maneuver to avoid having to think about the direction that they're heading."

The problem isn't AI itself, but the way it's designed. Algorithms are inherently Machiavellian; they will use any means to achieve their objective. With the wrong objective, Russell says, the consequences can be disastrous. "It's bad engineering."

Proposing a solution to AI's fundamental "design error" is the goal of Professor Russell's new book, which comes out in October. In advance of publication, we sat down to discuss the state of AI and how we can avoid plunging off the edge.

This conversation has been edited for length and clarity.   .... "

Reviving Artificial Intelligence at MIT

More on MIT's new center.   Is AI and its application to business back?

" ... A new interdisciplinary research center at MIT, funded by the National Science Foundation, aims at nothing less than unraveling the mystery of intelligence.

Artificial-intelligence research revives its old ambitions

The birth of artificial-intelligence research as an autonomous discipline is generally thought to have been the monthlong Dartmouth Summer Research Project on Artificial Intelligence in 1956, which convened 10 leading electrical engineers — including MIT’s Marvin Minsky and Claude Shannon — to discuss “how to make machines use language” and “form abstractions and concepts.” A decade later, impressed by rapid advances in the design of digital computers, Minsky was emboldened to declare that “within a generation ... the problem of creating ‘artificial intelligence’ will substantially be solved.” ... '