Marc Andreessen: Why AI Will Save the World

So we are OK, good to hear it ....    Great piece,  read it.

Why AI Will Save the World     by Marc Andreessen

AI, machine & deep learning  Generative AI

Fortunately, I am here to bring the good news: AI will not destroy the world, and in fact may save it.

First, a short description of what AI is: The application of mathematics and software code to teach computers how to understand, synthesize, and generate knowledge in ways similar to how people do it. AI is a computer program like any other – it runs, takes input, processes, and generates output. AI’s output is useful across a wide range of fields, ranging from coding to medicine to law to the creative arts. It is owned by people and controlled by people, like any other technology.

A shorter description of what AI isn’t: Killer software and robots that will spring to life and decide to murder the human race or otherwise ruin everything, like you see in the movies.

An even shorter description of what AI could be: A way to make everything we care about better.

Why AI Can Make Everything We Care About Better

The most validated core conclusion of social science across many decades and thousands of studies is that human intelligence makes a very broad range of life outcomes better. Smarter people have better outcomes in almost every domain of activity: academic achievement, job performance, occupational status, income, creativity, physical health, longevity, learning new skills, managing complex tasks, leadership, entrepreneurial success, conflict resolution, reading comprehension, financial decision making, understanding others’ perspectives, creative arts, parenting outcomes, and life satisfaction.

Further, human intelligence is the lever that we have used for millennia to create the world we live in today: science, technology, math, physics, chemistry, medicine, energy, construction, transportation, communication, art, music, culture, philosophy, ethics, morality. Without the application of intelligence on all these domains, we would all still be living in mud huts, scratching out a meager existence of subsistence farming. Instead we have used our intelligence to raise our standard of living on the order of 10,000X over the last 4,000 years.

What AI offers us is the opportunity to profoundly augment human intelligence to make all of these outcomes of intelligence – and many others, from the creation of new medicines to ways to solve climate change to technologies to reach the stars – much, much better from here.

AI augmentation of human intelligence has already started – AI is already around us in the form of computer control systems of many kinds, is now rapidly escalating with AI Large Language Models like ChatGPT, and will accelerate very quickly from here – if we let it.

In our new era of AI:

Every child will have an AI tutor that is infinitely patient, infinitely compassionate, infinitely knowledgeable, infinitely helpful. The AI tutor will be by each child’s side every step of their development, helping them maximize their potential with the machine version of infinite love.

Every person will have an AI assistant/coach/mentor/trainer/advisor/therapist that is infinitely patient, infinitely compassionate, infinitely knowledgeable, and infinitely helpful. The AI assistant will be present through all of life’s opportunities and challenges, maximizing every person’s outcomes.

Every scientist will have an AI assistant/collaborator/partner that will greatly expand their scope of scientific research and achievement. Every artist, every engineer, every businessperson, every doctor, every caregiver will have the same in their worlds.

Every leader of people – CEO, government official, nonprofit president, athletic coach, teacher – will have the same. The magnification effects of better decisions by leaders across the people they lead are enormous, so this intelligence augmentation may be the most important of all.

Productivity growth throughout the economy will accelerate dramatically, driving economic growth, creation of new industries, creation of new jobs, and wage growth, and resulting in a new era of heightened material prosperity across the planet.

Scientific breakthroughs and new technologies and medicines will dramatically expand, as AI helps us further decode the laws of nature and harvest them for our benefit.

The creative arts will enter a golden age, as AI-augmented artists, musicians, writers, and filmmakers gain the ability to realize their visions far faster and at greater scale than ever before.

I even think AI is going to improve warfare, when it has to happen, by reducing wartime death rates dramatically. Every war is characterized by terrible decisions made under intense pressure and with sharply limited information by very limited human leaders. Now, military commanders and political leaders will have AI advisors that will help them make much better strategic and tactical decisions, minimizing risk, error, and unnecessary bloodshed.

In short, anything that people do with their natural intelligence today can be done much better with AI, and we will be able to take on new challenges that have been impossible to tackle without AI, from curing all diseases to achieving interstellar travel.

And this isn’t just about intelligence! Perhaps the most underestimated quality of AI is how humanizing it can be. AI art gives people who otherwise lack technical skills the freedom to create and share their artistic ideas. Talking to an empathetic AI friend really does improve their ability to handle adversity. And AI medical chatbots are already more empathetic than their human counterparts. Rather than making the world harsher and more mechanistic, infinitely patient and sympathetic AI will make the world warmer and nicer. ...  (much more follows, with other input)  .... ' 

And further:

https://youtu.be/0wIUK0nsyUg

AI Risks, Debunked: The Conversation

Marc Andreessen and Martin Casado

From tireless collaborators for advancing scientific discovery, art, and government to powerful tools for solving problems like disease and climate change, AI has the capacity to dramatically improve our lives. We should embrace it... '  ... 

China Invests in Open Source Intelligence from Pentagon

 Christopher Ahlberg writes:

@cahlberg    Thrilled to see the @nytimes   write up on the great research by the Insikt team at 

@RecordedFuture  Quote Tweet The New York Times @nytimes Jun 1

China is investing deeply in open-source intelligence from the Pentagon, think tanks and private companies to learn more about the capabilities of the U.S. military in the Pacific and beyond, according to a new report. https://nyti.ms/43iXyZQ   ...  '  

Intelligence 3.0: Interview with Karl Friston, British Neuroscientist

Interesting and Thoughtful piece. Following up. 

KARL J. FRISTON - INTELLIGENCE 3.0        

https://youtu.be/V_VXOdf1NMw   https://youtu.be/V_VXOdf1NMw

64,533 views  Mar 11, 2023

This special edition show is sponsored by Numerai, please visit them here with our sponsor link (we would really appreciate it) http://numer.ai/mlst 

Prof. Karl Friston recently proposed a vision of artificial intelligence that goes beyond machines and algorithms, and embraces humans and nature as part of a cyber-physical ecosystem of intelligence. This vision is based on the principle of active inference, which states that intelligent systems can learn from their observations and act on their environment to reduce uncertainty and achieve their goals. This leads to a formal account of collective intelligence that rests on shared narratives and goals. 

To realize this vision, Friston suggests developing a shared hyper-spatial modelling language and transaction protocol, as well as novel methods for measuring and optimizing collective intelligence. This could harness the power of artificial intelligence for the common good, without compromising human dignity or autonomy. It also challenges us to rethink our relationship with technology, nature, and each other, and invites us to join a global community of sense-makers who are curious about the world and eager to improve it..... 

Russian Cyberwarfare Docs Leaked

 As usual interesting piece in Schneier.   Including useful commentary. We need to think of these ideas as a powerful form of warfare

Russian Cyberwarfare Documents Leaked    

Now this is interesting:  

Thousands of pages of secret documents reveal how Vulkan’s engineers have worked for Russian military and intelligence agencies to support hacking operations, train operatives before attacks on national infrastructure, spread disinformation and control sections of the internet.

The company’s work is linked to the federal security service or FSB, the domestic spy agency; the operational and intelligence divisions of the armed forces, known as the GOU and GRU; and the SVR, Russia’s foreign intelligence organisation.  

Lots more at the link.  ... 

Ukraine Has Digitized Its Fighting Forces on a Shoestring

Ukraine Has Digitized Its Fighting Forces on a Shoestring   By The Wall Street Journal, January 4, 2023

Ukrainian troops operated a telescopic tower with a remote camera on a Soviet car that was recast to observe and correct fire on the front line near Kharkiv, Ukraine, on Christmas Day.

Ukraine has digitally networked its fighting forces, intelligence, and weapons on the cheap through satellite communications and custom software.

Veterans of U.S. and allied digitization projects see Ukraine's patchwork command-and-control system as embodying the need to experiment and involve nonmilitary experts.

Ukraine's domestic technology-outsourcing industry and hacker community have proven invaluable by repurposing commercial technologies for the insurgency.

Examples include consumer drones modified to drop grenades, software to automate payroll on the front lines, and real-time battlefield intelligence relayed to local commanders from drones and spotters.

Ukrainian Minister of Digital Transformation Mykhailo Fedorov said on Twitter last week, “The enemy has been preparing for full-fledged [technology] war for 20 years. We made a technological leap in 10 months.”

From The Wall Street Journal   

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Re a Further Look at BioPhysics

How does life and physics fit together?    Just received in an Alumni mailing discussing,

THE PHYSICS OF Us

Physicists are studying how living matter works, and find that it breaks the standard rules and produces fascinating new phenomena.

Monday, December 5, 2022, By Susan Ahlborn. Illustrations by Marina Muun,   UPenn Omnia 

The James Webb telescope is showing us our universe in vibrant new detail. Some physicists, though, are looking in another direction: at us and other living matter here on Earth, from the cilia in lungs to the vasculature in leaves to the neurons in brains. What they’re finding is equally marvelous, and it’s challenging some of the current understanding of physics. 

Ultimately, they’re working to discover the rules that govern how matter lives and evolves, and their research may lead to better medicine, robotics based on biology, and an expanded understanding of the physical and biological world. 

How can an intelligent system arise from the collective dynamics of its basic components?

“We’re using physics principles to understand life and living matter,” says Eleni Katifori, Associate Professor of Physics and Astronomy, who studies vasculature in plants and animals. “But we are also using living matter as an inspiration to discover new physics, for asking the right questions or new questions.”

“Biology has already invented a lot of things. Living matter, from bacteria to leaves to humans, works in ways that physicists don’t understand, much less can duplicate,” adds Arnold Mathijssen, Assistant Professor of Physics and Astronomy. His goal is to unravel the physics of pathogens, design biomedical materials, and understand the collective functionality of living systems out of equilibrium. “It’s fundamental research. For example, how can an intelligent system arise from the collective dynamics of its basic components? It’s also directly relevant to our society, as in, what is the probability of SARS-CoV-2 transmission within a food supply chain?”

Leading the Way

The study of biophysics is not new; Luigi and Lucia Galvani were already investigating animal electricity in the late 1700s. In the last 20 years, though, technological advances have allowed researchers to see microscopic phenomena in living tissue with unprecedented detail, record simultaneously from thousands of neurons, and even track the large-scale behavior of ecosystems. All of these new methods produce vast amounts of quantitative data from which we can infer the laws of living matter. But it wasn’t until 2022 that the National Academies of Science, Engineering, and Medicine recognized biological physics as a separate field. 

Penn Arts & Sciences physicists have been studying living matter for decades, bringing Penn to the front of this area. Philip Nelson, Professor of Physics and Astronomy, wrote key books in the field, starting with Biological Physics: Energy, Information, Life in 2014. He’s been honored with the Emily Gray Award of the Biophysical Society for his “far-reaching and significant contributions.” Arjun Yodh, James M. Skinner Professor of Science, has received the Michael S. Feld Biophotonics Award of the Optical Society of America for his pioneering work in demonstrating and clinically translating biomedical optics. A.T. Charlie Johnson, Rebecca W. Bushnell Professor of Physics and Astronomy, is using biological molecules as chemical recognition elements in disease diagnosis, security, and environmental monitoring. Marija Drndic, Fay R. and Eugene L. Langberg Professor of Physics, explores mesoscopic and nanoscale structures, including the detection and analysis of DNA and microRNA.

In 2021, Penn Arts & Sciences and Penn Engineering made a unique investment in this interdisciplinary study with the new Center for Soft and Living Matter. Led by Director Andrea J. Liu, Hepburn Professor of Physics, and Associate Director Douglas J. Durian, Mary Amanda Wood Professor of Physics and Astronomy, the center brings together more than 60 faculty from the two schools. And Penn’s Computational Neuroscience Initiative, cofounded by Vijay Balasubramanian, Cathy and Marc Lasry Professor of Physics and Astronomy, involves researchers from Arts & Sciences, the Perelman School of Medicine, and Engineering. 

“If you look at a different scale or regime, new phenomena always pop up,” says Balasubramanian, a theoretical physicist who also holds a secondary appointment in neuroscience in the Perelman School of Medicine. “It’s the interactions between the components of living systems that make them so interesting, unlike, for example simple gases in a room. The brain contains a hundred billion interacting neurons. Molecules can talk to the whole organism, pheromones can change the behavior of entire colonies of organisms, stress can change gene expression. So, living systems interact across scales of organization unlike most physical systems that we are used to.”  .... ' 

AI Goal is not Intelligence?

Some thoughts ...

AI's true goal may no longer be intelligence  in ZDNet, Excerpts

Some scholars of AI warn that the present technologies may never add up to "true" intelligence or "human" intelligence. But much of the world may not care about that.

Written by Tiernan Ray, Contributing Writer on Oct. 28, 2022

AI has been rapidly finding industrial applications, such as the use of large language models to automate enterprise IT. Those applications may make the question of actual intelligence moot.

The British mathematician Alan Turing wrote in 1950, "I propose to consider the question, 'Can machines think?'" His inquiry framed the discussion for decades of artificial intelligence research.

For a couple of generations of scientists contemplating AI, the question of whether "true" or "human" intelligence could be achieved was always an important part of the work. 

The emergence of something called industrial AI in recent years may signal an end to such lofty preoccupations. AI has more capability today than at any time in the 66 years since the term AI was first coined by computer scientist John McCarthy. As a result, the industrialization of AI is shifting the focus from intelligence to achievement.

Also: OpenAI's Dall•E 2 may mean we never need stock photos again

Those achievements are remarkable. They include a system that can predict protein folding, AlphaFold, from Google's DeepMind unit, and the text generation program GPT-3 from startup OpenAI. Both of those programs hold tremendous industrial promise irrespective of whether anyone calls them intelligent. 

Among other things, AlphaFold holds the promise of designing novel forms of proteins, a prospect that has electrified the biology community. GPT-3 is rapidly finding its place as a system that can automate business tasks, such as responding to employee or customer queries in writing without human intervention.

That practical success, driven by a prolific semiconductor field, led by chipmaker Nvidia, seems like it might outstrip the old preoccupation with intelligence. 

In no corner of industrial AI does anyone seem to care whether such programs are going to achieve intelligence. It is as if, in the face of practical achievements that demonstrate obvious worth, the old question, "But is it intelligent?" ceases to matter.   ... ' 

Robot Dog and AI Research

Robotics of this types create excitement about the possibilities. 

Robot Dog Contributes to AI Research

By Government Technology, November 3, 2022

The University of Buffalo's School of Engineering and Applied Sciences recently acquired Yubie the robotic dog as a research platform to explore new frontiers in robotics, computer science, artificial intelligence, and other high-tech fields.

The University of Buffalo's (UB) Yubie, a robot dog with the brand name Spot the Agile Mobile Robot, is used as both for research and as an educational tool.

UB's Karthik Dantu said, "Every time we walk on campus, the amount of excitement it generates toward robotics is far beyond anything I can do by showing some slides or pictures. We would like to channel that to really excite anybody about AI [artificial intelligence] and robotics and other technologies that are coming."

This involves moving away from the controversy about the potential weaponization of robots and emphasizing the good that robots can do, from carrying heavy loads for troops and inspecting infrastructure to performing search-and-rescue missions and eventually serving as guide dogs and personal assistants.

UB's Dave Doermann explained, "These devices have narrow intelligence, so we can only design them to do specific things," adding "that's what people need to trust."

From Government Technology

View Full Article         

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 and Intelligence

 Musing on future directions. 

AI's true goal may no longer be intelligence

Some scholars of AI warn that the present technologies may never add up to "true" intelligence or "human" intelligence. But much of the world may not care about that.

Written by Tiernan Ray, Contributing Writer on Oct. 28, 2022

Illustration of a person with their face lifted out as a mask to show the computer inside, and wearing a nametag saying Hi, I'm AI

AI has been rapidly finding industrial applications, such as the use of large language models to automate enterprise IT. Those applications may make the question of actual intelligence moot.

The British mathematician Alan Turing wrote in 1950, "I propose to consider the question, 'Can machines think?'" His inquiry framed the discussion for decades of artificial intelligence research.

For a couple of generations of scientists contemplating AI, the question of whether "true" or "human" intelligence could be achieved was always an important part of the work. 

AI may now be at a turning point where such questions matter less and less to most people. 

The emergence of something called industrial AI in recent years may signal an end to such lofty preoccupations. AI has more capability today than at any time in the 66 years since the term AI was first coined by computer scientist John McCarthy. As a result, the industrialization of AI is shifting the focus from intelligence to achievement.

Also: OpenAI's Dall•E 2 may mean we never need stock photos again

Those achievements are remarkable. They include a system that can predict protein folding, AlphaFold, from Google's DeepMind unit, and the text generation program GPT-3 from startup OpenAI. Both of those programs hold tremendous industrial promise irrespective of whether anyone calls them intelligent. 

Among other things, AlphaFold holds the promise of designing novel forms of proteins, a prospect that has electrified the biology community. GPT-3 is rapidly finding its place as a system that can automate business tasks, such as responding to employee or customer queries in writing without human intervention.

That practical success, driven by a prolific semiconductor field, led by chipmaker Nvidia, seems like it might outstrip the old preoccupation with intelligence. 

In no corner of industrial AI does anyone seem to care whether such programs are going to achieve intelligence. It is as if, in the face of practical achievements that demonstrate obvious worth, the old question, "But is it intelligent?" ceases to matter.  ... ' 

Turing Test

Mostly agreed, it is mostly about deception rather than intelligence.   Rarely gives evidence for real reproducible intelligence for broad context. 

ACM OPINION

It's Time for AI to Retire the Turing Test

By Fortune, June 15, 2022

What to make of the strange case of Blake Lemoine? The Google AI engineer made headlines this past week when he claimed one of the company's chatbots had become "sentient." Not only does Google say Lemoine's claims are untrue, but almost every AI expert agrees that the chatbot, which Google calls LaMDA, is not sentient in the way Lemoine says it is.

Is Lemoine the inevitable result of the field's persistent fetishization of the Turing Test as a benchmark? In many actual versions of the Turing Test, humans often simply don't try that hard to stump the machine. And, in many cases, people are eager to deceive themselves into thinking the bots are real.

From Fortune  

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Power and Pitfalls of AI for US Intelligence.

Generalized but reasonable piece on the challenges involved.   More links in the original. 

The Power and Pitfalls of AI for U.S. Intelligence     By Wired

June 23, 2022

While artificial intelligence that can mimic humanlike sentience remains theoretical and impractical for most intelligence applications, machine learning is addressing fundamental challenges created by the volume and velocity of information that analysts  ... 

From cyber operations to disinformation, artificial intelligence extends the reach of national security threats that can target individuals and whole societies with precision, speed, and scale. As the U.S. competes to stay ahead, the intelligence community is grappling with the fits and starts of the impending revolution brought on by AI.

The U.S. intelligence community has launched initiatives to grapple with AI's implications and ethical uses, and analysts have begun to conceptualize how AI will revolutionize their discipline, yet these approaches and other practical applications of such technologies by the IC have been largely fragmented.

As experts sound the alarm that the U.S. is not prepared to defend itself against AI by its strategic rival, China, Congress has called for the IC to produce a plan for integration of such technologies into workflows to create an "AI digital ecosystem" in the 2022 Intelligence Authorization Act.

From Wired   

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Context In Machine Intelligence

I often make this point ... context is everything ... in fact I like to make the expansion of this as well:  'Context is everything', and its always changing"      Its like how we use all 'intelligence' that  is presented in a context, and included in that context are usually a number of uncontrolled time dimensions, like history and customer status and  other domain considerations, random context elements, etc  

Advancing Machine Intelligence: Why Context Is Everything  in TowardsDataScience  By Gadi Singer May 10

Most of us have heard the phrase, “Image is everything.” But when it comes to taking AI to the next level, it’s context that is everything.

Contextual awareness embodies all the subtle nuances of human learning. It is the ‘who’, ‘why’, ‘when’, and ‘why’ that inform human decisions and behavior. Without context, the current foundation models are destined to spin their wheels and ultimately interrupt the trajectory of expectation for AI to improve our lives.

This blog will discuss the significance of context in ML, and how late binding context could raise the bar on machine enlightenment.

Why Context Matters

Context is so deeply embedded in human learning that it is easy to overlook the critical role it plays in how we respond to a given situation. To illustrate this point, consider a conversation between two people that begins with a simple question: How is Grandma?

In a real-world conversation, this simple query could elicit any number of potential responses depending on contextual factors, including time, circumstance, relationship, etc.:

Fig 1. A proper answer to “How’s Grandma?” is highly context-dependent. 

The question illustrates how the human mind can track and take into account a vast amount of contextual information, even subtle humor, to return a relevant response. This ability to fluidly adapt to a variety of often subtle contexts is well beyond the reach of modern AI systems.

To grasp the significance of this deficit in machine learning, consider the development of reinforcement learning (RL)-based autonomous agents and robots. Despite the hype and success that RL-based architectures have had in simulated game environments like Dota 2 and StarCraft II, even purely gaming environments like NetHack pose a formidable obstacle to current RL systems due to the highly conditional nature and complexity of policies that are required to win the game. Similarly, as noted in many recent works, autonomous robots have miles to go before they can interact with previously unseen physical environments without the need of a serious engineering effort to either simulate the correct type of environment prior to deployment, or to harden the learned policy.

Current ML and Handling of Contextual Queries

With some notable exceptions, most ML models incorporate very limited context of a specific query, relying primarily on the generic context provided by the dataset that the model is trained or fine-tuned on. Such models also raise significant concerns about bias which makes them less suited for use in many business, healthcare, and other critical applications. Even state-of-the-art models like D3ST used in voice assistant AI applications require manually creating descriptions of schemata or ontologies with possible intents and actions that the model needs to identify context. While this involves a relatively minimal level of handcrafting, it nonetheless means that an explicit human input is required every time the context of the task is to be updated.

That’s not to say there haven’t been significant developments in context awareness for machine learning models. GPT-3, a famous large language model from the OpenAI team, has been used to generate full articles that rival human composition — a task that requires keeping track of at least local context. The Pathways Language Model (PaLM), introduced by Google in April 2022, demonstrates even greater capability, including the ability to understand conceptual combinations in appropriate contexts to answer complex queries.  ... 

Thoughts on Collaborative Intelligence

We have better tools, yes, but how well do they get to innovative solutions?  

How Collaborative Intelligence Can Boost Business Innovation, April 14, 2022

Ineffective meetings cost businesses $541 billion a year globally in lost productivity and employee time, according to Doodle’s 2019 State of the Meeting report. And according to Gallup’s State of the Global Workplace report, as much as 85% of employee time may be wasted on inefficient collaboration.

But the real costs of such disconnection go even deeper than money.

In a disconnected organization, innovation grinds to a halt. Individuals’ and teams’ great insights get trapped in silos. Employees feel stifled—unheard and disengaged. And with record numbers of knowledge workers quitting their jobs in the “Great Resignation,” the risks of disconnection are more urgent than ever.

Simply put: your organization can’t afford to have a disconnected workforce.

Building a company culture in which everyone is connected to their teams and has a shared sense of purpose is fundamental for collaboration and innovation. Building such a culture requires adopting collaborative intelligence (CI), a new systematic approach that connects teams to unlock their genius—taking insights and ideas from possibility to reality.

The Five Principles of Collaborative Intelligence

In the spirit of making collaboration intentional and purposeful, CI is comprised of three main components. First, there are collaboration spaces, both physical and online, where teams can connect. Second, there is collaboration design, the practice of introducing intentionality and technology to help teams connect and innovate. And finally, collaboration insights let organizations measure and improve their collaboration and innovation. Together, these yield a culture of collaboration where everyone feels connected and confident in delivering great work.

Collaborative intelligence is based on five principles:

People are greater together than apart.

Working in teams magnifies individuals’ power and value by coordinating a wider range of experience and expertise to tackle challenges and break down barriers to innovation and progress..... ' 

Invent the Work of the Future

 Good overview by Irving Wladawsky, including aspects of COVID influence, more at the links below. 

The Work of the Future Is Ours to Invent  Irving Wladawsky by IWB 

The Work of the Future:  Building Better Jobs in an Age of Intelligent Machines, by David Autor, David Mindell and Elisabeth Reynolds was published last week. The new book is based on the multiyear MIT Task Force on The Work of the Future that they jointly co-chaired.

The Task Force was commissioned by MIT President Rafael Reif in the spring of 2018 to address one of the most critical questions of the digital economy, - an economy that technology universities like MIT have played a major role in bringing about: as emerging technologies raise aggregate economic output and the wealth of nations, will they also enable people to attain higher living standards, better working conditions, greater economic security, and improved health and longevity? Led by Autor, Mindell and Reynolds, the task force involved over 20 faculty members from 12 departments at MIT as well as more than 20 graduate students. Its final report was released in November of 2020. In addition, the Task Force published a number of working papers and research briefs. ..... ' 

SAS Customer Intelligence Blog

 I have been reviewing reviewing SAS's blogs.  They nicely have a 'customer intelligence blog:

Customer Intelligence Blog

Evolving relationships for business growth

Welcome to Customer Intelligence, a blog for anyone who is looking for ways to improve the business of marketing and communicating with customers.

We strive to prompt new thinking in the way you tackle customer-related business issues. And we hope to inspire the use of analytics for everything from multi-level marketing to social media campaigns.  ... ' 

AI That Feels

 A space we examined with how products could link with consumers.   

BUILDING AN AI THAT FEELS

AI systems with emotional intelligence could learn faster and be more helpful

 By MARY CZERWINSKI, JAVIER HERNANDEZ, DANIEL MCDUFF in IEEE

IN THE PAST YEAR, have you found yourself under stress? Have you ever wished for help coping? Imagine if, throughout the pandemic, you'd had a virtual therapist powered by an artificial intelligence (AI) system, an entity that empathized with you and gradually got to know your moods and behaviors. Therapy is just one area where we think an AI system that can recognize and interpret emotions could offer great benefits to people.

Our team hails from Microsoft's Human Understanding and Empathy group, where our mission is to imbue technology with emotional intelligence. Why? With that quality, AI can better understand its users, more effectively communicate with them, and improve their interactions with technology. The effort to produce emotionally intelligent AI builds on work in psychology, neuroscience, human-computer interaction, linguistics, electrical engineering, and machine learning.

Lately, we've been considering how we could improve AI voice assistants such as Alexa and Siri, which many people now use as everyday aides. We anticipate that they'll soon be deployed in cars, hospitals, stores, schools, and more, where they'll enable more personalized and meaningful interactions with technology. But to achieve their potential, such voice assistants will require a major boost from the field of affective computing. That term, coined by MIT professor Rosalind W. Picard in a 1997 book by the same name, refers to technology that can sense, understand, and even simulate human emotions. Voice assistants that feature emotional intelligence should be more natural and efficient than those that do not.

Consider how such an AI agent could help a person who's feeling overwhelmed by stress. Currently, the best option might be to see a real human psychologist who, over a series of costly consultations, would discuss the situation and teach relevant stress-management skills. During the sessions, the therapist would continually evaluate the person's responses and use that information to shape what's discussed, adapting both content and presentation in an effort to ensure the best outcome.

While this treatment is arguably the best existing therapy, and while technology is still far from being able to replicate that experience, it's not ideal for some. For example, certain people feel uncomfortable discussing their feelings with therapists, and some find the process stigmatizing or time-consuming. An AI therapist could provide them with an alternative avenue for support, while also conducting more frequent and personalized assessments. One recent review article found that 1 billion people globally are affected by mental and addictive disorders; a scalable solution such as a virtual counselor could be a huge boon.

There's some evidence that people can feel more engaged and are more willing to disclose sensitive information when they're talking to a machine. Other research, however, has found that people seeking emotional support from an online platform prefer responses coming from humans to those from a machine, even when the content is the same. Clearly, we need more research in this area.

About 1 billion people globally are affected by mental disorders; a scalable solution such as an AI therapist could be a huge boon.

In any case, an AI therapist offers a key advantage: It would always be available. So it could provide crucial support at unexpected moments of crisis or take advantage of those times when a person is in the mood for more analytical talk. It could potentially gather much more information about the person's behavior than a human therapist could through sporadic sessions, and it could provide reminders to keep the person on track. And as the pandemic has greatly increased the adoption of telehealth methods, people may soon find it quite normal to get guidance from an agent on a computer or phone display.

For this kind of virtual therapist to be effective, though, it would require significant emotional intelligence. It would need to sense and understand the user's preferences and fluctuating emotional states so it could optimize its communication. Ideally, it would also simulate certain emotional responses to promote empathy and better motivate the person.

The virtual therapist is not a new invention. The very first example came about in the 1960s, when Joseph Weizenbaum of MIT wrote scripts for his ELIZA natural-language-processing program, which often repeated users' words back to them in a vastly simplified simulation of psychotherapy. A more serious effort in the 2000s at the University of Southern California's Institute for Creative Technologies produced SimSensei, a virtual human initially designed to counsel military personnel. Today, the most well-known example may be Woebot, a free chatbot that offers conversations based on cognitive behavioral therapy. But there's still a long way to go before we'll see AI systems that truly understand the complexities of human emotion.  .... ' 

Beyond Seti, Can AI get us There?

 A number of good questions are asked. 

AI Calling ET: How Artificial Intelligence Supports the Search for Extraterrestrial Life

By Karen Emslie,  Commissioned by CACM Staff,  September 28, 2021

Back in 1977, astronomer Jerry Ehman circled an anomaly on a printout of narrowband radio signal data recorded by Ohio State University's Big Ear telescope as it swept the skies for signs of extraterrestrial life. Alongside, Ehman wrote one now-famous word "Wow!"

We are still scouring the heavens for evidence that may answer the enduring question: are we alone in the universe? We don't yet know, but artificial intelligence (AI) is now supporting our investigations.

A number of methods have been deployed in the search for extraterrestrial life. They include in situ observations, like sending a rover to Mars to search for evidence of current or historical life, as well as remote sensing, like probing distant planetary atmospheres using technologies such as the future James Webb Space Telescope (JWST), and searching for signs of extraterrestrial technologies and communications.

AI is being used to support each of these research avenues.

Looking for planets that have similar life-supporting conditions to Earth, such as the presence of liquid water, is a vital part of the search. Exoplanets, or planets that orbit stars beyond our solar system, are a primary target. Explains Greg Olmschenk, a machine learning specialist at the U.S. National Aeronautics and Space Administration (NASA)  Goddard Institute for Space Sciences, "Our best bet right now for finding life elsewhere is to look for life as we know it, because that's the only kind of life that we know."

NASA missions, such the Transiting Exoplanet Survey Satellite (TESS) and the now-retired Kepler space telescope, survey the skies looking for exoplanets. Tiny dips in a star's perceived brightness can reveal the presence of an exoplanet as it transits, or crosses in front of, the star. These dips can be detected in light curves, or measurements of a star's light over time. TESS data, for example, contains around 60 million such light curves, which helps to explain the incorporation of AI into the process.

"It's not practical to have an astrophysicist look at each one of those and so we trained a neural network to do that," Olmschenk said.

To accomplish that, Olmschenk collaborated with researchers from the Universities Space Research Association, the Catholic University of America, the University of Maryland, and Science Systems and Applications, Inc. in the U.S., and the Università degli Studi di Napoli Federico II in Italy. They developed a one-dimensional (1D) convolutional neural network (CNN) to identify planetary transit signals. Olmschneck explained that while 2D convolutional networks are more common, as most neural networks are looking at images, "In this case, what we're looking at is one-dimensional time data."

Olmschenk said the CNN was trained to dismiss false positives, dips caused by other types of signals, such as the eclipsing of binary stars. It has a stack of convolutional layers that process input data, with each layer looking for certain patterns, such as light curves going up or down.

"You'll start to get pieces of the neural network that are recognizing peaks and troughs in the light curve, and eventually when you get down to the lower layers, you start to recognize things like actual transit dips."

The process filtered millions of light curves down to a few thousand that looked most likely to be planets. Astrophysicists then carried out follow-up observations to produce a short list of exoplanet candidates.

Sophisticated techniques such as radial velocity measurement, which detects the tell-tale 'wobble' produced by the gravitational tug of a planet on a star, ultimately determine whether candidates are truly planets. At the time of writing, 183 such candidates are still being investigated.

Searching for Signs of Intelligence

At the SETI Institute in Mountain View, CA, researchers have been looking for evidence of life on other worlds since 1985. The non-profit research organization was born from earlier SETI (search for extraterrestrial intelligence) projects funded by NASA.

SETI Institute CEO Bill Diamond draws a distinction between 'life' and 'intelligent life' on other worlds. While there may be biological life elsewhere in our solar system, he says, "We're talking about intelligent and technological beings that might exist on planets outside of our own solar system."

The Institute has close links with the nearby NASA Ames Research Center, and its researchers developed AI for NASA'S Kepler and TESS missions.  ... ' 

Will AI ever be Smarter than a Baby?

Thoughtful piece, with many links ...

By Irving Wladawsky-Berger

A collection of observations, news and resources on the changing nature of innovation, technology, leadership, and other subjects.

Will AI Ever Be Smarter Than a Baby?

The Ultimate Learning Machines - WSJI recently listened to a fascinating podcast where NY Times columnist Ezra Klein interviewed Berkeley psychologist Aliston Gopney     . Professor Gopney is best known for her research in cognitive science, particularly the study of children’s learning and development. She’s written extensively on the developmental phases of the human brain from babies to adults.

Gopnik, a member of the Berkeley AI Research group, has also been exploring the differences between human and machine intelligence, more specifically, what babies can teach us about AI. She’s long argued that babies and young children are smarter than we might think. In some ways, they’re even smarter than adults, let alone way smarter than the most advanced AIs.  

What do we mean by intelligence?

In 1994 the Wall Street Journal published the Mainstream Science on Intelligence, an article that included a definition that was agreed to by 52 leading academic researchers in fields associated with intelligence:

“Intelligence is a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience. It is not merely book learning, a narrow academic skill, or test-taking smarts. Rather, it reflects a broader and deeper capability for comprehending our surroundings - ‘catching on,’ ‘making sense’ of things, or ‘figuring out’ what to do.”

This is a very good definition of general intelligence, - the ability to effectively address a wide range of goals in different environments. It’s the kind of intelligence that’s long been measured in IQ tests, and that, for the foreseeable future, only humans have. On the other hand, specialized intelligence, - the ability to effectively address well-defined, specific goals in a given environment, - is the kind of task-oriented intelligence that’s part of many human jobs. Over the past decade, our increasingly capable AI systems have achieved or surpassed human levels of performance in selected applications including image and speech recognition, language translation, skin cancer classification, and breast cancer detection.

Psychologists have further identified two distinct types of human intelligence: fluid and crystallized. Fluid intelligence is the ability to quickly learn new skills, adapt to new environments and solve novel reasoning problems. It requires considerable raw processing power, generally peaks in our 20s and starts diminishing as we get older. Crystallized intelligence is the know-how and expertise which we accumulate over decades. It’s the ability to use our stocks of knowledge and experiences to make wise decisions. It generally increases through our 40s, peaks in our 50s, and does not diminish until late in life.  ... '

Podcast: How Spies Think

At the link, podcast provided by Recorded Future, who we worked with long ago.  The link below leads you to the podcast.  Makes me think:  How do spies think differently, say since before computing?  Is that useful from a security perspective?   Will listen to the podcast, and likely read the book. 

A Call to Arms In Favor of Rationality   By  Caitlin Mattingly Recorded Future

Our guest this week is Sir David Omand. He is former director of GCHQ, one of the UK’s primary intelligence agencies, and is currently Visiting Professor in War Studies, King’s College London.

We’ll be discussing his career in intelligence and public service, the changes he’s seen along the way, and we’ll discuss his most recent book How Spies Think: 10 Lessons from Intelligence.  ... 

This podcast was produced in partnership with the CyberWire.  ... '