Large Language Models: A Cognitive and Neuroscience Perspective

Irving does an excellent review and links to much work about LLMs, Large Language Models, and oter topics that are now much in the news. Below an intro. I plan to read all the articles pointed to at the link.  A considerable weakness in the current directions?   Implications to all this,  will provide.

A collection of observations, news and resources on the changing nature of innovation, technology, leadership, and other subjects.  By Irving Wladawsky-Berger  March 23, 2023

Large Language Models: A Cognitive and Neuroscience Perspective

Over the past few decades, powerful AI systems have matched or surpassed human levels of performance in a number of tasks such as image and speech recognition, skin cancer classification, breast cancer detection, and highly complex games like Go. These AI breakthroughs have been based on increasingly powerful and inexpensive computing technologies, innovative deep learning (DL) algorithms, and huge amounts of data on almost any subject. More recently, the advent of large language models (LLMs) are taking AI to the next level. And, for many technologists like me, LLMs and their associated chatbots have introduced us to the fascinating world of human language and cognition.

I recently learned the difference between form, communicative intent, meaning, and understanding from “Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data,” a 2020 paper by linguistic professors Emiliy Bender and Alexander Koller. These linguistic concepts helped me understand the authors’ argument that “in contrast to some current hype, meaning cannot be learned from form alone. This means that even large language models such as BERT do not learn meaning; they learn some reflection of meaning into the linguistic form which is very useful in applications.”

A few weeks ago, I came across another interesting paper, “Dissociating Language and Thought in Large Language Models: a Cognitive Perspective,” published in January, 2023 by principal authors linguist Kyle Mahowald and cognitive neuroscientist Anna Ivanova and four additional co-authors. The paper nicely explains how the study of human language, cognition and neuroscience sheds light on the potential capabilities of LLMs and chatbots. Let me briefly discuss what I learned.

“Today’s large language models (LLMs) routinely generate coherent, grammatical and seemingly meaningful paragraphs of text,” said the paper’s abstract. “This achievement has led to speculation that these networks are — or will soon become — thinking machines, capable of performing tasks that require abstract knowledge and reasoning. “Here, we review the capabilities of LLMs by considering their performance on two different aspects of language use: formal linguistic competence, which includes knowledge of rules and patterns of a given language, and functional linguistic competence, a host of cognitive abilities required for language understanding and use in the real world.

The authors point out that there’s a tight relationship between language and thought in humans. When we hear or read a sentence, we typically assume that it was produced by a rational person based on their real world knowledge, critical thinking, and reasoning abilities. We generally view other people’s statements not just as a reflection of their linguistic skills, but as a window into their mind. .... '

IBM and AI. An Abandonment of Watson?

 I see that our interesting correspondent and critic from before the first AI winter, Roger Schank, has posted a note strongly criticizing IBM on AI and cognitive claims.  Below an excerpt, link through to more: 

They are not doing "cognitive computing" no matter how many times they say they are  Update: February 2021

Commentng on WSJ article: IBM’s Retreat From Watson Highlights Broader AI Struggles in Health

I was chatting with an old friend yesterday and he reminded me of a conversation we had nearly 50 years ago. I tried to explain to him what I did for living and he was trying to understand why getting computers to understand was more complicated than key word analysis. I explained about concepts underlying sentences and explained that sentences used words but that people really didn’t use words in their minds except to get to the underlying ideas and that computers were having a hard time with that.

Fifty years later, key words are still dominating the thoughts of people who try to get computers to deal with language. But, this time, the key word people have deceived the general public by making claims that this is thinking, that AI is here, and that, by the way we should be very afraid, or very excited, I forget which.

We were making some good progress on getting computers to understand language but, in 1984, AI winter started. AI winter was a result of too many promises about things AI could do that it really could not do. (This was about promoting expert systems. Where are they now?). Funding dried up and real work on natural language processing died too.

But still people promote key words because Google and others use it to do "search". Search is all well and good when we are counting words, which is what data analytics and machine learning are really all about. Of course, once you count words you can do all kinds of correlations and users can learn about what words often connect to each other and make use of that information. But, users have learned to accommodate to Google not the other way around. We know what kinds of things we can type into Google and what we can’t and we keep our searches to things that Google is likely to help with. We know we are looking for texts and not answers to start a conversation with an entity that knows what we really need to talk about. People learn from conversation and Google can’t have one. It can pretend to have one using Siri but really those conversations tend to get tiresome when you are past asking about where to eat.

But, I am not worried about Google. It works well enough for our needs.

What I am concerned about are the exaggerated claims being made by IBM about their Watson program. Recently they ran an ad featuring Bob Dylan which made laugh, or would have, if had made not me so angry. I will say it clearly: Watson is a fraud. I am not saying that it can’t crunch words, and there may well be value in that to some people. But the ads are fraudulent. ... ' 

Virtual Reality to Assess Cognitive Abilities

 Heard of this kind of approach somewhere.   Either for determining brain and tactile health, or to use after specialized training, to assess abilities.   

Researchers create virtual reality cognitive assessment

by Center for BrainHealth in TechXplore

Virtual reality isn't just for gaming. Researchers can use virtual reality, or VR, to assess participants' attention, memory and problem-solving abilities in real world settings. By using VR technology to examine how folks complete daily tasks, like making a grocery list, researchers can better help clinical populations that struggle with executive functioning to manage their everyday lives. ... " 

Insights for AI from the Human Mind

Good thoughts by Gary Marcus, aiming at the difficulty of creating intelligence, even though we have very rich models around we can do testing with.  

Insights for AI from the Human Mind   By Gary Marcus, Ernest Davis

Communications of the ACM, January 2021, Vol. 64 No. 1, Pages 38-41  10.1145/3392663

What magical trick makes us intelligent? The trick is that there is no trick. The power of intelligence stems from our vast diversity, not from any single, perfect principle.

—Marvin Minsky, The Society of Mind

Artificial intelligence has recently beaten world champions in Go and poker and made extraordinary progress in domains such as machine translation, object classification, and speech recognition. However, most AI systems are extremely narrowly focused. AlphaGo, the champion Go player, does not know that the game is played by putting stones onto a board; it has no idea what a "stone" or a "board" is, and would need to be retrained from scratch if you presented it with a rectangular board rather than a square grid.

To build AIs able to comprehend open text or power general-purpose domestic robots, we need to go further. A good place to start is by looking at the human mind, which still far outstrips machines in comprehension and flexible thinking.

Here, we offer 11 clues drawn from the cognitive sciences—psychology, linguistics, and philosophy.

No Silver Bullets

All too often, people have propounded simple theories that allegedly explained all of human intelligence, from behaviorism to Bayesian inference to deep learning. But, quoting Firestone and Scholl,4 "there is no one way the mind works, because the mind is not one thing. Instead, the mind has parts, and the different parts of the mind operate in different ways: Seeing a color works differently than planning a vacation, which works differently than understanding a sentence, moving a limb, remembering a fact, or feeling an emotion."

The human brain is enormously complex and diverse, with more than 150 distinctly identifiable brain areas, approximately 86 billion neurons, hundreds if not thousands of different types; trillions of synapses; and hundreds of distinct proteins within each individual synapse.

Truly intelligent and flexible systems are likely to be full of complexity, much like brains. Any theory that proposes to reduce intelligence down to a single principle—or a single "master algorithm"—is bound to fail.

Rich Internal Representations

Cognitive psychology often focuses on internal representations, such as beliefs, desires, and goals. Classical AI did likewise; for instance, to represent President Kennedy's famous 1963 visit to Berlin, one would add a set of facts such as part-of (Berlin, Germany), and visited (Kennedy, Berlin, June 1963). Knowledge consists in an accumulation of such representations, and inference is built on that bedrock; it is trivial on that foundation to infer that Kennedy visited Germany.

Currently, deep learning tries to fudge this, with a bunch of vectors that capture a little bit of what's going on, in a rough sort of way, but that never directly represent propositions at all. There is no specific way to represent visited (Kennedy, Berlin, 1963) or part-of (Berlin, Germany); everything is just rough approximation. Deep learning currently struggles with inference and abstract reasoning because it is not geared toward representing precise factual knowledge in the first place. Once facts are fuzzy, it is difficult to get reasoning right. The much-hyped GPT-3 system1 is a good example of this.11 The related system BERT3 is unable to reliably answer questions like "if you put two trophies on a table and add another, how many do you have?" .... '   (much more follows) 

SAS Invests Big in AI

Another indication that the AI surge may not wither.   SAS knows its analytics, we used them  extensively, and it is investing heavily in AI tech.    Much different than in the last emergence of the idea.  Gets back to the definition of the term, which we debated at a talk last week.  My view: AI is more than JUST analytics,  it is analytics with cognitive, augmentative and autonomous capabilities added.  To make these ideas more readily implemented and delivered.

SAS to invest $1 billion in AI for industry uses, education, R&D
The AI investment is part of SAS' efforts to make data, AI, machine learning and algorithms more return driven and consumable.   By Larry Dignan in ZDNet

SAS said it will invest $1 billion in artificial intelligence over the next three years as it develops its analytics platform, educates data scientists and targets industry-specific use cases.

The investment is part of SAS' effort to build a higher profile. SAS is an analytics and data science pioneer, but the privately-held company has been quietly retooling its business and products.... "

Robots with Sense of Self?

We often found it useful, when modeling a conversation to introduce the 'self' aspect for concepts like memory, context and goals.    These were not for guiding physical robots, but robotic processes.    Still useful for noting and evaluating competitive goals and options.   Also elements of risk to individual 'selves'.  Might also be used in the 'digital twin' concepts when modeling human behavior?  Article below made me think of how the concept could be used.

Can robots ever have a true sense of self? Scientists are making progress  by Vishwanathan Mohan, in TechExplore

Having a sense of self lies at the heart of what it means to be human. Without it, we couldn't navigate, interact, empathise or ultimately survive in an ever-changing, complex world of others. We need a sense of self when we are taking action, but also when we are anticipating the consequences of potential actions, by ourselves or others.


Given that we want to incorporate robots into our social world, it's no wonder that creating a sense of self in artificial intelligence (AI) is one of the ultimate goals for researchers in the field. If these machines are to be our carers or companions, they must inevitably have an ability to put themselves in our shoes. While scientists are still a long way from creating robots with a human-like sense of self, they are getting closer.

Researchers behind a new study, published in Science Robotics, have developed a robotic arm with knowledge of its physical form – a basic sense of self. This is nevertheless an important step. ... " 

Microlearning and the Brain

Interesting idea for thinking about how to classify useful learning.

Microlearning and the Brain
Microlearning is effective for hard skills but detrimental when it comes to people and emotional skills.    by Todd Maddox in Clomedia

Microlearning abounds in the learning and development sector. However, there is confusion around the term’s use, and many incorrectly identify it as simply “short duration training.”

Microlearning is more accurately defined as:

An approach to learning that conveys information about a single, specific idea in a compact and focused manner.

A learning technique that operates within the learner’s working memory capacity and attention span, providing just enough information to allow the learner to achieve a specific, actionable objective.
For example, if a personnel manager was interested in obtaining information about unconscious bias, they might watch a brief piece of video content focused on the definition of unconscious bias and how it can affect leadership behaviors in the workplace. The information would be presented in two to three minutes and would convey a single idea with as few “extras” as possible. The short duration, singular focus and limited extras ensure the learner’s attention span and working memory capacity are not exceeded.

The overwhelming majority of L&D vendors market microlearning as a major component of their offering, as well they should.

Microlearning offers an ideal approach for engaging the cognitive skills learning system in the brain. The cognitive skills learning system is one of at least three learning systems in the brain that includes the emotional learning system and the behavioral skills learning system. A schematic of these three systems, along with the relevant brain structures, is displayed below.

The cognitive skills learning system relies on the prefrontal cortex, is limited by working memory and attentional processes, and is the primary system in the brain for learning hard skills. Combine microlearning with testing and targeted retraining that is spaced over time and you have a tool that speeds the transition from short-term memory in the prefrontal cortex to long-term memory in the hippocampus and fights against the brain’s natural tendency to forget. This allows you to train hard skills for retention. ... " 

When the Brain Switches Rules

An architectural approach for AI?    A concierge like approach?  Explicit rules, or just weights changed for perception, interaction?  Architectural implications.  An Opportunity for Context Clustering and Classification?

How the brain switches between different sets of rules
When you slow down after exiting the highway, or hush your voice in the library, you’re using this brain mechanism.   By Anne Trafton | MIT News Office 

Cognitive flexibility — the brain’s ability to switch between different rules or action plans depending on the context — is key to many of our everyday activities. For example, imagine you’re driving on a highway at 65 miles per hour. When you exit onto a local street, you realize that the situation has changed and you need to slow down. ... "

AI Overhyped? The Term is.

The problem is the term AI itself.   The assumption that it is far more than it is.   This does not mean you should not think about how smarter capabilities could be inserted into codes to augment our capabilities.  For a while we were using the term 'Cognitive Systems' to indicate methods closer and even mimicking human perception and abilities.  Probably be better to ditch 'AI' and go with Cognitive.  Though even the latter requires too much explanation and can be over emphasized.   Our Cognitive Systems Institute, monitored here,  attempts to emphasize cognitive aspects. Beware over-marketing.

 Artificial intelligence is often overhyped—and here’s why that’s dangerous

AI has huge potential to transform our lives, but the term itself is being abused in very worrying ways, says Zachary Lipton, an assistant professor at Carnegie Mellon University.
by Martin Giles

To those with long memories, the hype surrounding artificial intelligence is becoming ever more reminiscent of the dot-com boom.

Billions of dollars are being invested into AI startups and AI projects at giant companies. The trouble, says Zachary Lipton, is that the opportunity is being overshadowed by opportunists making overblown claims about the technology’s capabilities.

During a talk at MIT Technology Review’s EmTech conference today, Lipton warned that the hype is blinding people to its limitations. “It’s getting harder and harder to distinguish what’s a real advance and what is snake oil,” he said.

AI technology known as deep learning has proved very powerful at performing tasks like image recognition and voice translation, and it’s now helping to power everything from self-driving cars to translation apps on smartphones,

But the technology still has significant limitations. Many deep-learning models only work well when fed vast amounts of data, and they often struggle to adapt to fast-changing real-world conditions.

In his presentation, Lipton also highlighted the tendency of AI boosters to claim human-like capabilities for the technology. The risk is that the AI bubble will lead people to place too much faith in algorithms governing things like autonomous vehicles and clinical diagnoses.

“Policymakers don’t read the scientific literature,” warned Lipton, “but they do read the clickbait that goes around.” The media business, he says, is complicit here because it’s not doing a good enough job of distinguishing between real advances in the field and PR fluff.

Lipton isn’t the only academic sounding the alarm: in a recent blog post, “Artificial Intelligence—The Revolution Hasn’t Happened Yet,” Michael Jordan, a professor at University of California, Berkeley, says that AI is all too often bandied about as “an intellectual wildcard,” and this makes it harder to think critically about the technology’s potential impact. ... " 

Cognitive Services APIs from Microsoft

They have published a guides I am reviewing.  Note they are using the term Cognitive rather than AI, probably a good idea given the confusion.

Deliver a more personal experience to customers

Easily infuse your apps with Cognitive Services APIs that enhance productivity by seeing, hearing, speaking to, and comprehending your customers who interact with them.

In this free guide—Artificial Intelligence: A Developer's Guide to Getting Started with Microsoft Cognitive Services—get an overview of Cognitive Services and links to APIs that enable intelligent features in your apps, such as:

•    Emotion and video detection
•    Facial, speech, and vision recognition
•    Speech and language understanding  .... " 

(Update) Cognitive Assisted Interactive Labeling

Via: Karolyn Schalk,  Manager
Executive and Technical Expertise, Cloud, AI and IT Operations

CSIG Talk, July 5 10:30 AM ET: Tensorboard Speaker: Francois Luus, IBM

Speaker:   Francois Luus
Title:  “Cognitive-assisted Interactive Labeling & Software 2.0 “

Abstract: 
Future cognitive systems will largely make use of self-optimizing cognitive models, called Software 2.0, where the primary remaining task will be to provide labels/supervision. Software 2.0 will provide fully learned programs even for complex objectives and program functions, just requiring the human to specify the desired behavior. Human involvement in machine learning is thus evolving from engineering features, to hyperparameter optimization, and finally to providing supervision. In this talk we propose the use of machine learning itself in the Toolchain for Software 2.0, where the objective is to efficiently obtain and manage supervision. We modify a versatile SGD-based dimensionality reduction algorithm to allow for feature space quality assessment and for direct editing of the feature space itself, with labels as final output for use in image classifier training. We show the improvements in labeling efficiency through cognitive assistance for a variety of benchmark datasets.

Bio:  Francois Luus is a Research Scientist at the Johannesburg lab of IBM Research | Africa where machine learning is advanced and applied in the domains of healthcare, environment and finance. He holds a PhD in Computer Engineering and his research interests include computer vision, deep learning and dimensionality reduction, which led to innovations in Earth Observation during his time at the Council for Scientific and Industrial Research. Previously he conducted information-theoretic coding research at the Sentech Broadband Wireless Multimedia Center aimed at improving the robustness of wireless communications.

Zoom meeting Link: https://zoom.us/j/7371462221
Zoom Callin: (415) 762-9988 or (646) 568-7788 Meeting id 7371462221
Zoom International Numbers: https://zoom.us/zoomconference

(Check the website in case the date or time changes: http://cognitive-science.info/community/weekly-update/    Also access to slides and recording )

(Date Update) Talk: Vendor Agnostic Voice Assistants

Please note that this call has been postponed to August 9, 2018

Invitation to the ISSIP Cognitive Systems Institute Group Webinar

Please join us for this call and invite your contacts - e.g., at universities, partners & clients. The call is in a series - and you can see the series here http://cognitive-science.info/community/weekly-update/

Date and Time: August 9, 2018 - 10:30am US Eastern
Talk Title: Correcting Person Names for Automatic speech recognition (ASR) vendor-agnostic voice assistants

Speaker: Vijay Ramakrishnan, Tue Minh Vo, Cisco
.
Talk Description:  ASR systems trained on generic data often mis-transcribe domain-specific words and phrases. For voice assistants, errors in the ASR transcript cascade to the assistant's natural language understanding (NLU) components. We focus on the problem of ASR errors in person names and describe a novel method of correcting person names by leveraging a domain-specific language model (LM), and character and phoneme-based information retrieval (IR) techniques.

Bio: Vijay Ramakrishnan is a ML/NLP engineer at Cisco's Cognitive Collaboration Group where his team develops conversational AI products for Cisco's collaboration portfolio. His research interests include deep networks for domain-specific ASR, empirical methods for NLP and ML for sequence models.

Date and Time : August 9, 2018 - 10:30am US Eastern
Zoom meeting Link: https://zoom.us/j/7371462221
Zoom Callin: (415) 762-9988 or (646) 568-7788 Meeting id 7371462221
Zoom International Numbers: https://zoom.us/zoomconference
(Check the website in case the date or time changes: http://cognitive-science.info/community/weekly-update/ )

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Assessing the Application Landscape of Blockchains

Via Karolyn Schalk, Manager
Executive and Technical Expertise, Cloud, AI and IT Operations

April 5, 10:30 AM ET Assessing the application landscape of blockchain applications

Join us when Nico Abbatemarco, Research Associate of Practice on Information Systems and Digital Transformation at SDA Bocconi School of Management presents.

Zoom meeting Link: https://zoom.us/j/7371462221
Zoom Callin: (415) 762-9988 or (646) 568-7788 Meeting id 7371462221
Meeting slides and recordings, posted after the talk.

Talk Abstract:
Blockchain is emerging as a game changing technology in many industries. Although it is increasingly capturing the business community's attention, a comprehensive overview of commercially available applications was lacking to date. Our research aimed at fill this gap. Firstly, a structured approach to assess the application landscape of blockchain technologies will be proposed. To build our framework, we relied on largely accepted classifications of blockchains, on the base of protocols, consensus mechanisms and ownership type, as well as on the most cited application areas emerging from the literature. Then, the results emerged from a market analysis performed on a database of 460 released blockchains will be presented.

Short Bio:
 Nico Abbatemarco is a Research Associate of Practice on Information Systems and Digital Transformation at SDA Bocconi School of Management. At SDA Bocconi, he is part of the core team of the research Lab "Digital Enterprise Value and Organization" (DEVO Lab). His research activities focus on digital transformation and in particular on blockchain and distributed ledger technologies and their impact on the business community. Nico earned a Bachelor of Science in Finance and a Master of Science in Management both from Bocconi University in Milan. He has also been a visiting student at National Taiwan University in Taipei, Taiwan.  ....  "

Subconscious Choice

Reminder of work in the past, and Zaltman.   Back to measurement challenges.  Still being done, but to what value?  Read my previous tags from long ago.

Harvard Professor Says 95% of Purchasing Decisions Are Subconscious
When marketing a product to a consumer, it's most effective to target the subconscious mind.
By Logan Chierotti in Inc.

Why do consumers buy one product over another? How do you develop brand loyalty? How do you maximize customer engagement?  

According to Harvard professor Gerald Zaltman, the answer to all these questions is directly related to the subconscious mind. In Zaltman's book, "How Customers Think: Essential Insights into the Mind of the Market," the professor reveals many exciting ideas that can be helpful to marketers and brands. .... "

Inferring Emotion and Cognitive Changes

The OBAIS department at the Lindner College of Business, University of Cincinnati, invites you to attend a research seminar:

Date and time: Wednesday, March 28th, 2018, 11:00AM-12:00PM

Location: Lindner Hall 608
Speaker: Prof. Joe Valacich, Eller Professor in MIS, University of Arizona

Title: Inferring Emotion and Cognitive Changes through Human-Computer Interaction Devices: From Basic Research to Communalization
......

Best wishes,

Yichen Qin, Assistant Professor
Department of Operations, Business Analytics, and Information Systems
Lindner College of Business, University of Cincinnati
Website: http://business.uc.edu/academics/departments/obais/faculty/qinyn.html
Email: qinyn@ucmail.uc.edu

Fujitsu Human Centric AI

Was impressed with Fujitsu's work in retail when we visited.

Fujitsu drives a human centric model

AI is a core technology which enables many complex processes to be conducted independently of human judgment. Now, deep learning is often featured in the media. But it is not the whole story of AI, just an important piece of the puzzle. Our human cognition is continuously generated from complex interactions between our sensory organs, nervous system, brain and external environments.

To achieve an AI, we have to replicate and bring together a range of cognitive capabilities: perceiving, reasoning, making choices, learning, communicating, and moving and manipulating.

Fujitsu is developing key technologies under a comprehensive framework (see diagram). We call it Human Centric AI, Zinrai. Fujitsu is incorporating component technology such as machine learning, deep learning and visual recognition, into its digital solutions and services. .... " 

How P&G and American Express Are Approaching AI

I am quoted in the Harvard Business Review about how P&G successfully used AI in the past to improve systems, including estimates of actual value.     This HBR article has just been reposted, and the complete article is for sale if you don't have a subscription .... Ask me for more about these efforts.  Much supporting information has also been posted here.   More details were also published in the Cognitive Systems Institute archives.

How P&G and American Express Are Approaching AI
By Thomas H. Davenport, Randy Bean

Published March 31, 2017

There is a tendency with any new technology to believe that it requires new management approaches, new organizational structures, and entirely new personnel. That impression is widespread with cognitive technologies — which comprises a range of approaches in artificial intelligence (AI), machine learning, and deep learning. Some have argued for the creation of “chief cognitive officer” roles, and certainly many firms are rushing to hire experts with deep learning expertise. “New and different” is the ethos of the day. .... 

Two good examples of combining well-established practices with cognitive technology to achieve business success are American Express and Procter & Gamble. Both firms are actively undertaking cognitive technology initiatives.  Both are well into their second centuries; they wouldn’t still be here if they weren’t able to accommodate change well and introduce new technology effectively. We spoke with top executives at each of these firms about the rise of cognitive in their organizations. Ash Gupta is President of Global Credit Risk and Information Management at American Express, and Guy Peri is Chief Data Officer and Vice President of Information Technology at P&G. Both executives have longstanding track records of success at their respective organizations, having seen business and technology change come and go for 20 years or more.

How it will impact business, industry, and society.

Both organizations have a considerable history with artificial intelligence. Gupta at American Express reminded us of the Authorizer’s Assistant, which was one of the more successful rule-based expert systems of the late 1980s. As described in a popular Harvard Business Review article on that generation of technology, the system made recommendations to human authorizers whether to approve large purchase transactions by cardholders.

P&G also built and employed a number of rule-based expert systems. In addition to Peri, the current CDO, we also spoke with Franz Dill, a retired P&G IT manager who focused on AI during the 80s and 90s. He said that the most well-known expert system they developed was one that blended Folgers coffee (no longer a P&G brand). This system, Dill noted, saved P&G in excess of $20 million dollars a year in green coffee costs. The company also built an expert system that helped advertisers at P&G to use, modify, and reuse the company’s advertising assets.

Both American Express and P&G are companies that have explored artificial intelligence over the years, and while the technology may have changed, the established yet innovative approaches that these firms take to incorporating new technologies and capabilities continues to evolve. Their fundamentally sound innovation practices provide a foundation for evolution. The attributes of their respective approaches to cognitive technology include .... " 

Blockchain and Artificial Intelligence

Below is from the talk mentioned above from our Linked Cognitive Systems Group.  Join us in future talks, many will be mentioned here.

Blockchain and Artificial Intelligence  by Dr. Vugranam (VC) Sreedhar

Abstract: In this talk I will briefly introduce deep connection between the underlying models of blockchain and artificial intelligence (AI) ..... 

Here are the slides from Dr. Vugranam's Lecture.  Quite technically oriented, but also some general embedded gems.  Following up:  http://cognitive-science.info/wp-content/uploads/2018/02/CSIG_Vugranam_Sreedhar.BlockChain-AI-02-22-2018-v1.pdf

(Update):  And more by Dr Vugranam, this more developer and detail oriented, includes specific information about architecture of Smart Contract blockchain approaches:  https://www.youtube.com/watch?v=qnUBzE9CQqg--

Innovation Vision of the Cognitive Era

This recorded session presents a complete innovative vision to the cognitive era ...

By Hesham Soultan,  Senior IT Architect at IBM

Further discussion at the Linkedin: Cognitive Institute Group  Join us.

AI in the Enterprise

AI Begins to Infiltrate the Enterprise  in InformationWeek

Enterprise adoption of AI is slow today, but experts expect it to increase very rapidly. So far, tech giants are leading the charge.

Despite a flood of publicity and product announcements related to artificial intelligence, it seems that few enterprises have adopted the technology so far.

Status Report: AI in the Enterprise

Whit Andrews, vice president and distinguished analyst at Gartner, was able to put some hard numbers to the trend. "We are in the very earliest stages of enterprise adoption of artificial intelligence," he said. "Specifically, in our most recent CIO survey from 2017, one in 25 CIOs described themselves as having artificial intelligence in action in their organizations."

The companies farthest along with the technology tend to be technology giants, said Hadley Reynolds, managing director and co-founder of the Cognitive Computing Consortium. These companies are "basing much of their businesses on various kinds of machine learning and deep learning technologies," he said, so they  .... "