After having been following the IBM Watson effort from its very beginning, it appears to be emerging via advertising again. See my mentions of 'IBM Watson' in this blog. My enterprise was approached by IBM, as a possible collaborator to support corporate decision making. We had some useful applications. We talked, but not much more. Too expensive, hard to test and too incomplete for our needs. Our experiments were all internal. They had been exploring Language models, and were successful in demonstrating their use, but not as as broadly and easily testable as what has been seen in GPT efforts in the last few years. Why not? Will we see their general emersion soon? Are they about to take the bite? Exploring? I will be posting more here. Join us? - Franz Dill
Friday, April 07, 2023
AI and Human Error: Root Causes and Mitigation Strategies
Unintentional Human errors and Mitigation Strategies
Excerpt.
AI and human error: Root causes and mitigation strategies in Venturebeat
Taylor Hersom, Eden Data, April 7, 2023
Algorithms and doorway concept with wavy and straight linesJoin top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Learn More
Leave any preconceptions you may have about AI at the door. If you can get past the futuristic image that the media constructs about AI, you can find real business value: machine learning (ML) models that solve real-world business problems.
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From cybersecurity, governance and compliance, and accounting to navigating a recession and managing data, talent, and workloads, AI is here to stay. Its main goals are automation, agility and speed. The limitations of human performance and the impact of human error are unquestionably top AI innovation drivers.
Join us in San Francisco on July 11-12, where top executives will share how they have integrated and optimized AI investments for success and avoided common pitfalls.
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How AI can help press the right button
A slip, a lapse, a mix-up. Who has not pressed the wrong button when doing a repetitive task, even if they are highly skilled? Unintentional errors are common in a wide range of industries. They occur in environments where procedures and processes are well-established and automated.
Measuring human error’s global economic and social impact on all industries is a virtually impossible task. But we can rapidly visualize the severe risks involved when, for example, we meditate on the consequences of human error in sectors like healthcare, where lives are on the line. Even Chernobyl — one of the most dangerous nuclear incidents in modern history — began with a human error, followed by a flawed risk management plan.
Unintentional human errors can slow performance, disrupt normal production operations and even lead to injuries and death. In response, smart industrial AI-driven platforms are used to detect irregularities in production and distribution systems and flag them before they occur.
How do these platforms work? In the fourth industrial revolution, automation is powered by a network of industrial IoT devices that constantly relay data to an edge gateway, which in turn uploads it to the cloud. In the cloud, AI systems analyze the data for rapid visualization, risk prevention and predictive analysis.
These AI systems can “learn” and improve performance by removing gaps while “fixing” the root causes that lead to human errors.
On the other hand, mistakes also occur when workers are subject to stressful conditions and experience burnout. “Everyone can make errors no matter how well trained and motivated they are,” says the Health and Safety Executive (HSE), Britain’s national regulator for workplace health and safety. ... '
An Architecture that Combines Deep Neural networks and Vector-Symbolic Models
New AI Architecture
An architecture that combines deep neural networks and vector-symbolic models by Ingrid Fadelli , in Tech Xplore
Researchers at IBM Research Zürich and ETH Zürich have recently created a new architecture that combines two of the most renowned artificial intelligence approaches, namely deep neural networks and vector-symbolic models. Their architecture, presented in Nature Machine Intelligence, could overcome the limitations of both these approaches, solving progressive matrices and other reasoning tasks more effectively.
"Our recent paper was based on our earlier research works aimed at augmenting and enhancing neural networks with the powerful machinery of vector-symbolic architectures (VSAs)," Abbas Rahimi, one of the researchers who carried out a study, told Tech Xplore. "This combination was previously applied to few-shot learning as well as few-shot continual learning tasks, achieving state-of-the-art accuracy with lower computational complexity. In our recent paper, we take this concept beyond perception, by focusing on solving visual abstract reasoning tasks, specifically, the widely used IQ tests known as Raven's progressive matrices."
Raven's progressive matrices are non-verbal tests typically used to test people's IQ and abstract reasoning skills. They consist in a series of items presented in sets, where one or more item is missing.
To solve Raven's progressive matrices, respondents need to correctly identify the missing items in given sets among a few possible choices. This requires advanced reasoning capabilities, such as being able to detect abstract relationships between objects, which could be related to their shape, size, color, or other features.
The neuro-vector-symbolic architecture (NVSA) developed by Rahimi and his colleagues combines deep neural networks, which are known to perform well on perception tasks, with VSA machinery. VSAs are unique computational models that perform symbolic computations using distributed, high-dimensional vectors.
"While our approach might sound a bit like neuro-symbolic AI approaches, neuro-symbolic AI has inherited the limitations of their individual deep learning and classical symbolic AI components," Rahimi explained. "Our key objective is to address these limitations, namely the neural binding problem and exhaustive search, in NVSA by using a common language between the neural and symbolic components."
The team's combination of deep neural networks and VSAs was supported by two main architecture design features. These include a new neural network training process and a method to perform VSA transformations.
"We developed two key enablers of our architecture," Rahimi said. "The first is the use of a novel neural network training method as a flexible means of representation learning over VSA. The second is a method to attain proper VSA transformations such that exhaustive probability computations and searches can be substituted by simpler algebraic operations in the VSA vector space."
In initial evaluations, the architecture developed by Rahimi and his colleagues attained very promising results, solving Raven's progressive matrices faster and more efficiently than other architectures developed in the past. Specifically, it performed better than both state-of-the-art deep neural networks and neuro-symbolic AI approaches, achieving new record accuracies of 87.7% on the RAVEN dataset and 88.1% on the I-RAVEN dataset.
"To solve a Raven test, something called probabilistic abduction is required, a process that involves searching for a solution in a space defined by prior background knowledge about the test," Rahimi said. "The prior knowledge is represented in symbolic form by describing all possible rule realizations that could govern the Raven tests. The purely symbolic reasoning approach needs to go through all valid combinations, compute the rule probability, and sum them up. This search becomes a computational bottleneck in the large search space, due to a large number of combinations that would be prohibitive to test."
In contrast with existing architectures, NVSA can perform extensive probabilistic calculations in a single vector operation. This in turn allows it to solve abstract reasoning and analogy-related problems, such as Raven's progressive matrices, faster and more accurately than other AI approaches based on deep neural networks or VSAs alone.
"Our approach also addresses the neural binding problem, enabling a single neural network to separately recognize distinct properties of multiple objects simultaneously in a scene," Rahimi said. "Overall, NVSA offers transparent, fast and efficient reasoning; and it is the very first example showing how probabilistic reasoning (as an upgrade of pure logical reasoning) can be efficiently performed by distributed representations and operators of VSA. Compared to the symbolic reasoning of neuro-symbolic approaches, the probabilistic reasoning of NVSA is two orders of magnitude faster, with less expensive operations on the distributed representations. ...'
More information: Michael Hersche et al, A neuro-vector-symbolic architecture for solving Raven's progressive matrices, Nature Machine Intelligence (2023). DOI: 10.1038/s42256-023-00630-8 Journal information: Nature Machine Intelligence
Serious Errors can be Made with ChatGPT
Care must be taken, security introduced. Ease of use can mean ease of error.
Samsung workers made a major error by using ChatGPT in TechRadar By Lewis Maddison published 3 days ago
Samsung meeting notes and new source code are now in the wild after being leaked in ChatGPT
Samsung workers have unwittingly leaked top secret data whilst using ChatGPT to help them with tasks.
The company allowed engineers at its semiconductor arm to use the AI writer to help fix problems with their source code. But in doing so, the workers inputted confidential data, such as the source code itself for a new program, internal meeting notes data relating to their hardware.
The upshot is that in just under a month, there were three recorded incidences of employees leaking sensitive information via ChatGPT. Since ChatGPT retains user input data to further train itself, these trade secrets from Samsung are now effectively in the hands of OpenAI, the company behind the AI service.
Out in the OpenAI
In response, Samsung Semiconductor is now developing its own inhouse AI for internal use by employees, but they can only use prompts that are limited to 1024 bytes in size.
In one of the aforementioned cases, an employee asked ChatGPT to optimize test sequences for identifying faults in chips, which is confidential - however, making this process as efficient as possible has the potential to save chip firms considerable time in testing and verifying processors, leading to reductions in cost too.
In another case, an employee used ChatGPT to convert meeting notes into a presentation, the contents of which were obviously not something Samsung would have liked external third parties to have known.
Samsung Electronics sent out a warning to its workers on the potential dangers of leaking confidential information in the wake of the incidences, saying that such data is impossible to retrieve as it is now stored on the servers belonging to OpenAI. In the semiconductor industry, where competition is fierce, any sort of data leak could spell disaster for the company in question. ... '
Thursday, April 06, 2023
Walmart to Add EV Chargers
Spreading availability.
Walmart to add EV chargers to thousands of US stores
Walmart plans to add electric vehicle chargers to thousands of stores across the United States by 2030.
Walmart announced Thursday plans to add electric vehicle (EV) chargers to thousands of US stores in a sign of further mainstreaming of emission-free autos.
The US retail giant, which has EV chargers at a fraction of its US stores, plans to "build our own EV fast-charging network at thousands of Walmart and Sam's Club locations coast-to-coast," said a Walmart news release, noting that approximately 90 percent of the US population lives within 10 miles of a Walmart.
The announcement is the latest sign of movement in the US transportation market in what is expected to be a years-long transition away from the internal combustion engine car.
Electric vehicles accounted for 5.8 percent of the US market for new vehicles in 2021, up from 3.2 percent in 2021, according to Cox Automotive. .... '
Fraunhofer and Digital Medicine
Fraunhofer at the DMEA
Fraunhofer to showcase digital healthcare of tomorrow
Press Release / April 05, 2023
Improved patient care, speedier diagnoses and savings in care costs – the digital transformation of the healthcare industry promises solutions to the urgent problems of our time. Increasingly, systems based on artificial intelligence (AI) are being employed. But how can these be used effectively while maintaining compliance with data protection laws? Experts from the Fraunhofer-Gesellschaft will be providing insights into their current work at DMEA 2023 in Berlin and answering questions about tomorrow’s health IT at booth D107 in Hall 2.2.
Fraunhofer to showcase digital healthcare of tomorrow
Health research occupies a prominent position at the Fraunhofer-Gesellschaft. Together with partners from the world of medicine, a number of institutes are working to develop digital solutions for the prevention of disease as well as the diagnosis, therapy and rehabilitation of patients. The aim is to streamline processes and also to make affordable care available for an increasingly aging society. The resulting technologies support players in the healthcare sector, such as clinics and medical staff, as well as patients by providing them with applications for use at home.
Apps and applications for optimized and individualized treatment
The Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS will be demonstrating AI-based software for the automated processing of medical documents. Advances in AI research are enabling the reliable and comprehensible use of large-scale language models (foundation models) for applications such as the generation of physicians’ letters, information extraction and billing. This saves time in everyday clinical practice and guarantees high-quality treatment.
The Fraunhofer Institute for Computer Graphics Research IGD is making an active contribution to personalized medicine with digital solutions. Its software applications support specialist staff and patients in prevention, diagnostics and therapy. These include a new method for allergy test evaluation on a smartphone and the option of performing visual-interactive data analysis based on cohorts, for example in the area of chronic inflammatory bowel disease. The Guardio® AI-based software package converts heart movements into an ECG while the patient’s own smartphone is placed in proximity to the chest.
The main concern of the Fraunhofer Institute for Cognitive Systems IKS is trust and efficiency when artificial intelligence is employed in medical care. The research team will be presenting new quantum computing approaches in the context of AI to improve the early detection of breast cancer. They will also be showing how routine clinical data and AI can be reliably combined to support doctors in their decision making.
In modern clinical practice, intelligent data integration is becoming increasingly important for medical staff. With the help of new algorithms and innovative AI, more precise diagnoses and personalized therapy plans can be created automatically.
At the DMEA, the Fraunhofer Institute for Digital Medicine MEVIS will present software solutions for data structuring and guideline-based decision-making to interested companies. In addition, MEVIS experts will demonstrate optimized image-based follow-up of cancer treatments using AI.
The Fraunhofer Center for Digital Diagnostics addresses the improvement of medical care in rural areas through patient-oriented diagnostics and digitalization. Data discontinuities in patient care are analyzed and optimized. Healthcare in rural areas is facilitated by the development of a fully automated, mobile health center. Next-generation virus tests will allow for needs-based diagnostics and outbreak containment. Intelligent wound care will enable faster healing of festering wounds.
In Portugal, the Fraunhofer Center for Assistive Information and Communication Solutions AICOS develops technologies for digital healthcare, in which predictive, preventive, personalized and participatory medicine plays a key role. The team will be presenting the results of its work to the German health market at the DMEA. The technologies developed facilitate human intervention, connectivity and collaboration in healthcare. In the matter of decentralized healthcare, the Fraunhofer Center has set itself the goal of improving access to early treatment, supporting clinical decisions with the help of algorithms, generating transparent and explainable AI-based decisions, and minimizing bias and unfairness. ... '
Bing’s AI chatbot is now on your Android phone’s keyboard
Was an early user of Bings AI Chatbot, relatively good, now yet more accessible.
Bing’s AI chatbot is now on your Android phone’s keyboard — here’s how to get it
By Peter Hunt Szpytek, April 6, 2023
Chatbots have become extraordinarily popular for those looking for help with their writing — or simply mess around with them and have fun. Now, Android users have access to one such chatbot straight from their keyboard. Bing Chat is Microsoft’s chatbot that rivals the likes of ChatGPT and Google Bard, and it has been added as a feature to the SwiftKey keyboard, a predictive keyboard that helps with texting.
While Bing Chat can help users compose the body of a message, it can also analyze prewritten text for your tone to suggest changes if the user wants to avoid coming across a certain way. Bing Chat has already been providing those services via the Bing app and through web browsers; however, the new integration with the SwiftKey keyboard allows users to easily access the chatbot for help whenever their keyboard is enabled. ... '
Seagull Algorithms and Cloud Computing.
New to me. Have a strong interest in bird flight and further behavior. Biomimicry
ACM TECHNEWS
Seagull Algorithms Could Hide Secret to Greener Cloud Computing, By Fast Company, April 5, 2023
Modeling the motions of seagulls.
The researchers say that the way seagulls behave when they’re on the hunt for food or prey is one of the most ruthlessly efficient examples of an entity zeroing in on its target, with minimal excess energy expenditure.
British, Chinese, and Austrian researchers say that more sustainable cloud computing systems can be achieved by algorithmically mimicking seagulls' hunting and migration behavior.
The researchers contend a "seagull optimization algorithm" can slash cloud computing's power consumption by 5.5% and its network traffic by 70%.
They said the meta-heuristic algorithm would determine the best locations for virtual machines within a server communications network to optimize network traffic efficiency.
This would mirror the seagulls' ability to stalk and plot a route to prey without colliding with each other.
The researchers believe seagull-based algorithms could reduce the total number of physical supercomputers worldwide, while also cutting the machine-to-machine communications network's power consumption by 80%.
From Fast Company
View Full Article
Robot Caterpillar Demonstrates Locomotion Approach for Soft Robotics
Robot Caterpillar Demonstrates Locomotion Approach for Soft Robotics
By NC State University News, March 28, 2023
How the caterpillar-like soft robot moves.
The caterpillar-bot consists of two layers of polymer that respond differently when exposed to heat. The bottom layer shrinks, or contracts, when exposed to heat. The top layer expands when exposed to heat.
North Carolina State University (NC State) researchers have created a caterpillar-like soft robot that can locomote forward, backward, and duck under cramped areas.
Uniquely patterned silver nanowires control the robot's bending motion with programmable heat so users can steer it in either direction.
Two polymer layers constitute the caterpillar-bot, with the bottom layer contracting when exposed to heat while the top layer expands.
The researchers can apply electric current to different lead points to control which sections of the nanowire pattern heat up, and regulate the amount of heat according to how much current is expended.
NC State's Yong Zhu said, "This approach to driving motion in a soft robot is highly energy efficient, and we're interested in exploring ways that we could make this process even more efficient."
From NC State University News
More New AI: FreedomGPT, Controversy OK?
More AI, Open Source, with some interesting aspects. Allows controversial topics? Will try to examine.
By Tanya Malhotra -April 4, 2023
Also on Reddit
Large Language Models have rapidly gained enormous popularity by their extraordinary capabilities in Natural Language Processing and Natural Language Understanding. The recent model which has been in the headlines is the well-known ChatGPT. Developed by OpenAI, this model is famous for imitating humans for having realistic conversations and does everything from question answering and content generation to code completion, machine translation, and text summarization.
ChatGPT comes with censorship compliance and certain safety rules that don’t let it generate any harmful or offensive content. A new language model called FreedomGPT has recently been introduced, which is quite similar to ChatGPT but doesn’t have any restrictions on the data it generates. Developed by the Age of AI, which is an Austin-based AI venture capital firm, FreedomGPT answers questions free from any censorship or safety filters.
FreedomGPT has been built on Alpaca, which is an open-source model fine-tuned from the LLaMA 7B model on 52K instruction-following demonstrations released by Stanford University researchers. FreedomGPT uses the distinguishable features of Alpaca as Alpaca is comparatively more accessible and customizable compared to other AI models. ChatGPT follows OpenAI’s usage policies which restrict categories like hate, self-harm, threats, violence, sexual content, etc. Unlike ChatGPT, FreedomGPT answers questions without bias or partiality and doesn’t hesitate to answer controversial or argumentative topics. ... '
Wednesday, April 05, 2023
As AI Continues to Surpass Human Performance, it's Time to Reevaluate Tests,
Evaluating Performance by AI, Humans. Implications? Most interesting. Well take worth a closer look.
As AI continues to surpass human performance, it's time to reevaluate tests , says expert Shana Lynch, Stanford University
Credit: Pixabay/CC0 Public Domain
How good is AI? According to most of the technical performance benchmarks we have today, it's nearly perfect. But that doesn't mean most artificial intelligence tools work the way we want them to, says Vanessa Parli, associate director of research programs at the Stanford Institute for Human-Centered AI and a member of the AI Index steering committee.
She cites the current popular example of ChatGPT. "There's been a lot of excitement, and it meets some of these benchmarks quite well," she said. "But when you actually use the tool, it gives incorrect answers, says thing we don't want it to say, and is still difficult to interact with."
In the newest AI Index, published on April 3, a team of independent researchers analyzed over 50 benchmarks in vision, language, speech, and more to find out that AI tools are able to score extremely high on many of these evaluations.
"Most of the benchmarks are hitting a point where we cannot do much better, 80-90% accuracy," she said. "We really need to be thinking about how we, as humans and society, want to interact with AI, and develop new benchmarks from there."
In this conversation, Parli explains more about the benchmarking trends she sees from the AI Index.
What do you mean by benchmark?
A benchmark is essentially a goal for the AI system to hit. It's a way of defining what you want your tool to do, and then working toward that goal. One example is HAI Co-Director Fei-Fei Li's ImageNet, a dataset of over 14 million images. Researchers run their image classification algorithms on ImageNet as a way to test their system. The goal is to correctly identify as many of the images as possible.
What did the AI Index study find regarding these benchmarks?
We looked across multiple technical benchmarks that have been created over the past dozen years— around vision, around language, etc.—and evaluated the state-of-the-art result in each benchmark year over a year. So, for each benchmark, were researchers able to beat the score from last year? Did they meet it? Or was there no progress at all? We looked at ImageNet, a language benchmark called SUPERGlue, a hardware benchmark called MLPerf, and more; some 50 were analyzed and over 20 made it into the report.
And what did you find in your research?
In earlier years, people were improving significantly on the past year's state of the art or best performance. This year across the majority of the benchmarks, we saw minimal progress to the point we decided not to include some in the report. For example, the best image classification system on ImageNet in 2021 had an accuracy rate of 91%; 2022 saw only a 0.1 percentage point improvement.
So we're seeing a saturation among these benchmarks—there just isn't really any improvement to be made.
Additionally, while some benchmarks are not hitting the 90% accuracy range, they are beating the human baseline. For example, the Visual Question Answering Challenge tests AI systems with open-ended textual questions about images. This year, the top performing model hit 84.3% accuracy. Human baseline is about 80%. ... '
AI is Teaching us New, Surprising Things About the Human Mind
ACM TECHNEWS
AI is Teaching Us New, Surprising Things About the Human Mind
By The Wall Street Journal, April 5, 2023
A researcher demonstrates the brain-scanning magnetoencephalography device at New York University.
Artificial intelligence is helping scientists decode how neurons in our brains communicate, and to explore the nature of cognition.
Scientists are gaining new insights into the human mind though artificial intelligence (AI), including the mechanism of communication between neurons, and the roots of cognition.
The University of California, Berkeley's Celeste Kidd and colleagues used a clustering model to find people's opinions tend to diverge about even the most fundamental properties of things. Researchers led by Princeton University's Tatiana Engel used artificial neurons to interpret hundreds of neurons' electrical impulses in animals' brains simultaneously, then trained them to perform identical tasks.
These networks self-organize into reasonable approximations of those in animals, indicating dynamic electrical activity forms the substance of thought, according to Engel.
From The Wall Street Journal
View Full Article - May Require Paid Subscription
On the Age of AI
Interview on the Age of AI
Author Talks: In the ‘age of AI,’ what does it mean to be smart? in McKinsey
March 16, 2023 | Interview
As artificial intelligence gets better at predicting human behavior, a business psychologist encourages people to strengthen the uniquely human skills that machine learning has yet to tap.
McKinsey Global Private Markets Review 2023: Private markets turn down the volume
In this edition of Author Talks, McKinsey Global Publishing’s Raju Narisetti chats with Tomas Chamorro-Premuzic about his new book, I, Human: AI, Automation, and the Quest to Reclaim What Makes Us Unique (Harvard Business Review Press, February 2023). Chamorro-Premuzic explains why some AI algorithms model humanity as a simple species, how attention has become commoditized, and why the right questions are now more valuable than the right answers. An edited version of the conversation follows.
Why did you write this, your 12th book, now?
I’m a professor of business psychology at Columbia University and UCL [University College London] and the chief innovation officer at ManpowerGroup. I, Human: AI, Automation, and the Quest to Reclaim What Makes Us Unique is a book about the behavioral consequences or impact of artificial intelligence, including the dark side of human behavior and what we should do to upgrade ourselves as a species.
The book is written at a time that, in my view, could only be described as the AI age. Humans have always relied on technological inventiveness and innovation to shape their cultural and social evolution, and I think there can be very little doubt that the definitive technology of today is artificial intelligence, or AI.
Now, even the wider public is talking about things like ChatGPT and other conversational interfaces, and the tech giants are described mostly as data companies and as algorithmic prediction businesses.
The book was very much written in the midst of the AI age, or under the influence of AI, because I wrote the bulk of this at the height of the pandemic when we had very little physical interaction or contact with other people outside of our nuclear families. This means I was heavily influenced by hyperconnectedness and the datafication of me. Everything I did was being datafied and subjected to the predictive powers of AI during 2020 and 2021.
I can’t say that there won’t be a better era to read the book, but it certainly wouldn’t have had the same connotation and impact if we had published it five or ten years ago.
Haven’t humans always blamed technology for every problem they face?
There is a common tendency for people to overreact to things that are novel, whether in a good way or in a bad way, and technologies are a very good example of this.
Perhaps the best example is how, when the written newspaper first scaled up and productized, people feared that humans would never meet in person ever again because there would be no information or even gossip to exchange if all the news was in written form. Also, from the 1950s onward, people showed concern that television would lead to less intellectual activities, but I don’t think they were wrong because reading habits went down since mass TV was introduced.
What I tried to do with this book is not be at one extreme or the other. What’s important to me is to not miss the opportunity to highlight the behavioral impact and consequences that we have already seen artificial intelligence have on us. This is not a book about AI, but about humans in the AI age.
Although a lot of what I highlight is about the dark side of behaviors that AI has unleashed, there are also some great opportunities that have had very positive effects on us—on both an individual and collective level.
What is the ‘crisis of distractibility’? ( Edited at Authors Request, read more at link)
Self-Aware AI
Advanced thoughts.
Why Should We Look Forward to Self-Aware AI?
by bigdata 28 February 2023 Artificial Intelligence, Machine Learning Tags: Artificial intelligence, chatbots, chatGPT, IoT, machine learning, Predictive Analytics, Robotic Process Automation, Robots
Technology AI
Many experts believe that the era of the self-aware AI is still far ahead in the future. They say that robotic sentience is still highly theoretical and needs ongoing research. However, several technologists and roboticists have claimed to having developed sentient machines.
So, we have to ask, is it really that far off in the future? Or, has the so-called experts just been remiss in agreeing on standards to define true robotic sentience? In any case, there is a lot of look forward to when it comes to self-aware machines.
Sentient Machines: The Pros
The present and future developments in robotic sentience have several advantages that we should all look forward to.
Improved efficiency and productivity: There is incredible potential for highly intelligent machines to provide industries with better productivity and efficiency. Machines are faster at processing and doing repetitive tasks, and do not theoretically need rest, unlike humans.
Better problem-solving capabilities: With super-fast processing and access to vast data, and a presumed lack of biases, smart machines can potentially solve more problems that humans can ever imagine to.
Advances in healthcare: Self-aware machines can potentially provide faster and more accurate diagnosis and more personalized treatments to individuals. Through big data, they can catch symptoms and diagnose diseases faster. Such time advantage can make a huge difference in a person’s treatment and recovery.
Better scientific research: When it comes to scientific experimentation and discoveries, fast processing is key. There is a lot of ground to cover when you want to be thorough and cover all test possibilities. A super smart machine can do this, easily. It is what it’s designed to do. ... '
How Experts Think
We captured expertise from observations, then linked it with neural networks.
Eye Tracking During Building Inspections Provides Insight on How Experts Think By Penn State News, March 30, 2023
Penn State's Maria antonieta Gutierrez Soto uses Tobii eye-tracking glasses to assess damages in row buildings in Mayfield, Kentucky after the region endured tornadoes in December 2021.
Researchers at Pennsylvania State University (Penn State) and Chariho Regional High School in Rhode Island used eye-tracking software to analyze building inspectors' gaze patterns to gain insights into safety assessment behaviors.
The researchers had architectural engineering graduate students evaluate two building facades while tracking their gaze with Tobii glasses, which measure eye movement and positioning concurrently.
Penn State's Rebecca Napolitano said the results reflected users' biases when looking at structures, and a tendency to focus on observable issues longer compared to other fields. She called the research "one small step in understanding how inspectors think, with the long-term goal of creating an algorithm to inform a drone."
From Penn State News
Tuesday, April 04, 2023
A Practical AI Glossary
From https://www.futuretools.io/ and being updated, and can be searched at the link
"FutureTools Collects & Organizes All The Best AI Tools
Search - Try things like "YouTube" or "SEO" to find specific tools for your needs...
---------------
Search Glossary (and being updated)
Application Programming Interface(API):
An API, or application programming interface, is a set of rules and protocols that allows different software programs to communicate and exchange information with each other. It acts as a kind of intermediary, enabling different programs to interact and work together, even if they are not built using the same programming languages or technologies. API's provide a way for different software programs to talk to each other and share data, helping to create a more interconnected and seamless user experience.
Artificial Intelligence(AI):
the intelligence displayed by machines in performing tasks that typically require human intelligence, such as learning, problem-solving, decision-making, and language understanding. AI is achieved by developing algorithms and systems that can process, analyze, and understand large amounts of data and make decisions based on that data.
Compute Unified Device Architecture(CUDA):
CUDA is a way that computers can work on really hard and big problems by breaking them down into smaller pieces and solving them all at the same time. It helps the computer work faster and better by using special parts inside it called GPUs. It's like when you have lots of friends help you do a puzzle - it goes much faster than if you try to do it all by yourself.
The term "CUDA" is a trademark of NVIDIA Corporation, which developed and popularized the technology.
Data Processing:
The process of preparing raw data for use in a machine learning model, including tasks such as cleaning, transforming, and normalizing the data.
Deep Learning(DL):
A subfield of machine learning that uses deep neural networks with many layers to learn complex patterns from data.
Embedding:
When we want a computer to understand language, we need to represent the words as numbers because computers can only understand numbers. An embedding is a way of doing that. Here's how it works: we take a word, like "cat", and convert it into a numerical representation that captures its meaning. We do this by using a special algorithm that looks at the word in the context of other words around it. The resulting number represents the word's meaning and can be used by the computer to understand what the word means and how it relates to other words. For example, the word "kitten" might have a similar embedding to "cat" because they are related in meaning. Similarly, the word "dog" might have a different embedding than "cat" because they have different meanings. This allows the computer to understand relationships between words and make sense of language.
Feature Engineering:
The process of selecting and creating new features from the raw data that can be used to improve the performance of a machine learning model.
Freemium:
You might see the term "Freemium" used often on this site. It simply means that the specific tool that you're looking at has both free and paid options. Typically there is very minimal, but unlimited, usage of the tool at a free tier with more access and features introduced in paid tiers.
Generative Adversarial Network(GAN):
A type of computer program that creates new things, such as images or music, by training two neural networks against each other. One network, called the generator, creates new data, while the other network, called the discriminator, checks the authenticity of the data. The generator learns to improve its data generation through feedback from the discriminator, which becomes better at identifying fake data. This back and forth process continues until the generator is able to create data that is almost impossible for the discriminator to tell apart from real data. GANs can be used for a variety of applications, including creating realistic images, videos, and music, removing noise from pictures and videos, and creating new styles of art.
Generative Art:
Generative art is a form of art that is created using a computer program or algorithm to generate visual or audio output. It often involves the use of randomness or mathematical rules to create unique, unpredictable, and sometimes chaotic results.
Generative Pre-trained Transformer(GPT):
GPT stands for Generative Pretrained Transformer. It is a type of large language model developed by OpenAI.
Giant Language model Test Room(GLTR):
GLTR is a tool that helps people tell if a piece of text was written by a computer or a person. It does this by looking at how each word in the text is used and how likely it is that a computer would have chosen that word. GLTR is like a helper that shows you clues by coloring different parts of the sentence different colors. Green means the word is very likely to have been written by a person, yellow means it's not sure, red means it's more likely to have been written by a computer and violet means it's very likely to have been written by a computer.
GitHub:
GitHub is a platform for hosting and collaborating on software projects
Google Colab:
Google Colab is an online platform that allows users to share and run Python scripts in the cloud
Graphics Processing Unit(GPU):
A GPU, or graphics processing unit, is a special type of computer chip that is designed to handle the complex calculations needed to display images and video on a computer or other device. It's like the brain of your computer's graphics system, and it's really good at doing lots of math really fast. GPUs are used in many different types of devices, including computers, phones, and gaming consoles. They are especially useful for tasks that require a lot of processing power, like playing video games, rendering 3D graphics, or running machine learning algorithms.
Langchain:
LangChain is a library that helps users connect artificial intelligence models to external sources of information. The tool allows users to chain together commands or queries across different sources, enabling the creation of agents or chatbots that can perform actions on a user's behalf. It aims to simplify the process of connecting AI models to external sources of information, enabling more complex and powerful applications of artificial intelligence.
Large Language Model(LLM):
A type of machine learning model that is trained on a very large amount of text data and is able to generate natural-sounding text.
Machine Learning(ML):
A method of teaching computers to learn from data, without being explicitly programmed.
Natural Language Processing(NLP):
A subfield of AI that focuses on teaching machines to understand, process, and generate human language
Neural Networks:
A type of machine learning algorithm modeled on the structure and function of the brain.
Neural Radiance Fields(NeRF):
Neural Radiance Fields are a type of deep learning model that can be used for a variety of tasks, including image generation, object detection, and segmentation. NeRFs are inspired by the idea of using a neural network to model the radiance of an image, which is a measure of the amount of light that is emitted or reflected by an object.
OpenAI:
OpenAI is a research institute focused on developing and promoting artificial intelligence technologies that are safe, transparent, and beneficial to society
Overfitting:
A common problem in machine learning, in which the model performs well on the training data but poorly on new, unseen data. It occurs when the model is too complex and has learned too many details from the training data, so it doesn't generalize well.
Prompt:
A prompt is a piece of text that is used to prime a large language model and guide its generation
Python:
Python is a popular, high-level programming language known for its simplicity, readability, and flexibility (many AI tools use it)
Reinforcement Learning:
A type of machine learning in which the model learns by trial and error, receiving rewards or punishments for its actions and adjusting its behavior accordingly.
Spatial Computing:
Spatial computing is the use of technology to add digital information and experiences to the physical world. This can include things like augmented reality, where digital information is added to what you see in the real world, or virtual reality, where you can fully immerse yourself in a digital environment. It has many different uses, such as in education, entertainment, and design, and can change how we interact with the world and with each other.
Stable Diffusion:
Stable Diffusion generates complex artistic images based on text prompts. It’s an open source image synthesis AI model available to everyone. Stable Diffusion can be installed locally using code found on GitHub or there are several online user interfaces that also leverage Stable Diffusion models.
Supervised Learning:
A type of machine learning in which the training data is labeled and the model is trained to make predictions based on the relationships between the input data and the corresponding labels.
Unsupervised Learning:
A type of machine learning in which the training data is not labeled, and the model is trained to find patterns and relationships in the data on its own.
Webhook:
A webhook is a way for one computer program to send a message or data to another program over the internet in real-time. It works by sending the message or data to a specific URL, which belongs to the other program. Webhooks are often used to automate processes and make it easier for different programs to communicate and work together. They are a useful tool for developers who want to build custom applications or create integrations between different software systems.
Supply Chain Transparency and Sustainability
Whats important in Supply Chain
Why Supply Chain Transparency And Sustainability Are So Important Right Now
AUTOMATION ROBOTS SUPPLY CHAIN SUSTAINABILITY TRANSPARENCY
Apr 04, 2023, by Ron Margulis, with further expert comment
Robots were everywhere last month at ProMat, a major material handling trade show, along with were autonomous guided vehicles, artificial intelligence, inventory and network optimization software and mobile technology.
Extended supply chain transparency and sustainability were frequently discussed in educational sessions and on the show floor.
Nearly three-quarters of supply chain leaders are increasing their supply chain technology and innovation budgets this year, according to the 2023 MHI Annual Industry Report, released at ProMat in collaboration with Deloitte. The report titled “The Responsible Supply Chain: Transparency, Sustainability, and the Case for Business” indicates that solutions for improved supply chain transparency and sustainability are getting top priority. .. '
Norstrom and Panera Using AI
AI Usage examples
Nordstrom and Panera Leverage AI and Automation to Enhance Operations
AI AUTOMATION NORDSTROM PANERA BREAD
Mar 30, 2023, by Melissa Minkow
Alexis DePree, chief supply chain officer of Nordstrom, and George Hanson, VP/chief digital officer of Panera Bread, sat down on Monday at Shoptalk in Las Vegas to discuss the bright future that retail can anticipate leveraging artificial intelligence (AI) and automation.
Both leaders agreed that the key to optimizing operations comes down to leveraging AI and automation for low-value, repeatable tasks so that employees are freed up to engage with customers at a high-value, ad hoc level. Not only does embracing technology in this way improve the customer experience, but it also empowers the workforce, driving employee satisfaction.
Given Ms. DePree’s focus area, most of the AI and automation-based wins she shared were related to logistics. Nordstrom has been leaning into AI and automation to design unique routing depending on the order, improve rates of return, and maximize storage space.
“The supply chain used to be set it and forget it. Now it differs with each order,” said Ms. DePree.
She explained that there is now more complexity in the supply chain and automation has allowed for its movement from a cost center to a value driver. Additionally, since storage density has significantly increased, warehouses are much more economical thanks to the retailer’s advanced technology.
Panera’s use of AI and automation enables more personalized digital and dining experiences. Mr. Hanson specifically highlighted the brand’s reliance on AI-powered voice recognition in the drive-thru. He also pointed to the use of AI to accommodate customers whose orders require repair by proactively offering a range of options.
Further, he mentioned how crucial technology is in alleviating “microfrictions” when ordering in the cafe. Panera has recalibrated its use of AI and automation in online ordering, drive-thru and in-cafe operations. Mr. Hanson emphasized that properly allocating resources across experiences has ensured that winning, AI-based app features are brought to life in the cafe ... '
SAS on Data Driven to AI Driven
Talk: https://blogs.sas.com/content/sascom/2023/03/27/from-data-driven-to-ai-driven-scaling-human-productivity-and-decision-making/
https://blogs.sas.com/content/sascom/2023/03/27/from-data-driven-to-ai-driven-scaling-human-productivity-and-decision-making/
From data-driven to AI-driven: Scaling human productivity and decision making
By Bryan Harris on SAS Voices March 27, 2023 topics | Analytics Artificial Intelligence
Given the headlines each week, it is clear that global disruption and economic volatility are not slowing down. At the same time, information overload is far exceeding human capacity.
Despite these pressures, business goals remain the same: improve revenue, increase margins, operate more efficiently and meet customer expectations. So, how do we keep up and, most importantly, get ahead in today’s world? Businesses must scale human productivity and decision making with AI to ultimately discover the future faster.
In the video below, I discuss the challenges organizations face and how they can become more resilient with AI-driven strategies.
https://blogs.sas.com/content/sascom/2023/03/27/from-data-driven-to-ai-driven-scaling-human-productivity-and-decision-making/
To learn more strategies for business resilience, check out our new Resiliency Rules Report.
Tagsdata culture and literacydata strategydigital disruptioninnovation
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ABOUT AUTHOR Bryan Harris
Executive Vice President & Chief Technology OfficerWebsiteLinkedIn
As Executive Vice President and Chief Technology Officer, Bryan Harris is responsible for setting the technology direction for SAS and working with the executive leadership team to translate the organization’s strategic objectives and priorities into products and solutions. Harris has more than 20 years of experience researching and developing analytic techniques, enterprise search technologies, distributed computing and cloud architectures, and user experiences for both the federal and commercial industries. For nearly 10 years, he has been a critical senior leader of SAS R&D. ....
Monday, April 03, 2023
How and When the Chip Shortage Will End
As we use more AI to train and solve, increasingly important.
How and When the Chip Shortage Will End, in 4 Charts Fabs using older process nodes are the key SAMUEL K. MOORE
ONE LOOMING ARTIFACT of the pandemic that remains in 2023—the global chip shortage—has gratefully begun to recede. Unlike the state of things in mid-2021—when crimps in the semiconductor supply chain cropped up in big ways—supply and demand have become much less of a mismatch.
As IEEE Spectrum reported in the months since this story originally posted, the broken supply chains caused by the chip shortage have practically rewired whole segments of the tech industry. For the automotive industry, as we summarized in five charts that helped demystify the chip shortage, time eventually brought carmakers up from the end of a 52-week waiting list to get the chips they needed for their entertainment and driving-assistance systems. With chips finally reaching factory floors, their own manufacturing capacities were restored to prepandemic levels by the end of 2022.
Meanwhile, the mid-2022 passage of the CHIPS Act in the United States yielded a multibillion-dollar investment pool, some of which was dedicated to ramping up American manufacturing of the mature-generation chips upon which many industries—auto and otherwise—are so dependent. In March of 2023, the U.S. began disbursing CHIPS Act funding, while the E.U. considered getting into the chip-stimulus game as well .... '
How Generative AI Will Change Sales
Thinking of testing this in the real world where enough data exists.
How Generative AI Will Change Sales by Prabhakant Sinha, Arun Shastri, and Sally E. Lorimer, March 31, 2023
Sales teams have typically not been early adopters of technology, but generative AI may be an exception to that. Sales work typically requires administrative work, routine interactions with clients, and management attention to tasks such as forecasting. AI can help do these tasks more quickly, which is why Microsoft and Salesforce have already rolled out sales-focused versions of this powerful tool. close
Last month, Microsoft fired a powerful salvo by launching Viva Sales, an application with embedded generative AI technology designed to help salespeople and sales managers draft tailored customer emails, get insights about customers and prospects, and generate recommendations and reminders. A few weeks later, Salesforce (the company) followed by launching Einstein GPT.
Sales, with its unstructured, highly variable, people-driven approach, has been a laggard behind functions such as finance, logistics, and marketing when it comes to utilizing digital technologies. But now, sales is primed to quickly become a leading adopter of generative AI — the form of artificial intelligence used by OpenAI (the company behind ChatGPT) and its competitors. AI-powered systems are on the way to becoming every salesperson’s (and every sales manager’s) indispensable digital assistant.
Sales is well-suited to the capabilities of generative AI models. Selling is interaction and transaction intensive, producing large volumes of data, including text from email chains, audio of phone conversations, and video of personal interactions. These are exactly the types of unstructured data the models are designed to work with. The creative and organic nature of selling creates immense opportunities for generative AI to interpret, learn, link, and customize.
But to realize the true potential, there are hurdles and challenges to overcome. Generative AI must be non-intrusively embedded into sales processes and operations so sales teams can naturally integrate the capabilities into their workflow. Generative AI sometimes draws wrong, biased, or inconsistent conclusions. Although the publicly accessible models are valuable (hundreds of millions of users like us have already used ChatGPT to query the knowledge base on practically every topic), the true power for sales teams comes when models are customized and fine-tuned on company-specific data and contexts. This can be expensive and requires scarce expertise, including people with significant knowledge of AI and sales. So how can sales organizations harvest the value without wasting energy on heading down unproductive pathways?
What’s Possible
Before addressing the how, consider what generative AI can do for sales organizations.
Reversing administrative creep. Almost every sales organization we touch is cursed with the gradual increase of administrative work over time. As selling complexity grows, so does the need for documentation, approvals, and compliance reporting. Unwittingly, the increasing use of sales technology is also a large factor. New technologies often lead to more training, more data entry, and more reports to peruse. Generative AI can reverse administrative creep, for example, by helping salespeople write emails, respond to proposal requests, organize notes, and automatically update CRM data.
Enhancing salespeople’s customer interactions. The use of AI in sales has been progressing of late. We have helped many companies deploy AI-powered systems that recommend personalized content and product offers, along with the best channel for salespeople to use to connect with customers. Recommendations are based on data about the preferences and behaviors of the customer and similar customers, as well as past interactions with the customer. Salespeople accept or reject the recommendations and can rate their quality to improve the algorithms.
By layering on generative AI, the models can produce better recommendations. One example would be considering customer sentiments gleaned from the nuances of language and subtle signals of customer interest or distrust — in emails, conversations with salespeople, posts on social media sites, and more. Further, the salesperson can collaborate with the system to improve recommendations in real-time. For example, after receiving a suggestion to approach a customer with a new offering, the salesperson can dig deeper — both vertically into the customer’s own needs and horizontally to find other customers who might benefit from the same offering. An interactive, conversational user interface makes the application easy to use. In a truly collaborative seller-buyer environment, even the buyer can be part of the dialog.
Assisting sales managers. Sales managers spend a lot of time studying reports and analytics on sales performance. Recently, most sales reports have progressed from passive, backward-looking documents to more interactive, diagnostics tools with drill-down capabilities. With generative AI, reporting systems can become even more powerful and forward-looking. Managers can pose questions to get insights for helping salespeople improve and for delivering more pointed and motivational coaching feedback. Sales planning tasks that took weeks can be performed in an hour, as managers dialog with the system to discover opportunities, formulate key account strategies, and determine how to allocate effort to geographies, customers, products, and activities. ... '
On Cubesats Tech
Sunday, April 02, 2023
Select Plugin Partners with ChatGPT
The list makes sense for initial collaborators, but most any provider of services could be here.
Via Digital Trends:
Select plugin partners to GPT with OpenAI
OpenAI just announced that ChatGPT is getting even more powerful with plugins that allow the AI to access portions of the internet. This expansion could simplify tasks like shopping and planning trips without the need to access various websites for research.
This new web integration is in testing with select partners at the moment. The list includes Expedia, FiscalNote, Instacart, Kayak, Klarna, Milo, OpenTable, Shopify, Slack, Speak, Wolfram, and Zapier. ... '
An AI Drummer Robot
ACM NEWS
Meet The Band's New Drummer: Keirzo the Robot
By The Lighthouse, Macquarie University (Australia), March 30, 2023
Macquarie University's Richard Savery playing along with Keirzo the drummer robot.
Macquarie University's Richard Savery said Keirzo the drummer robot learns from human musician; "Through inbuilt microphones, it listens to drummers, and then it tries to adjust the way it plays."
Keirzo is a vaguely humanoid-shaped construction with ear-like sound inputs, a speaker where a mouth might be and a range of motor-driven robotic 'arms,' some fitted with drumsticks, others with a small round rubber mallet. Keirzo's music - including rap lyrics - is produced entirely by the robot, says Richard Savery, who is a developer of artificial intelligence and robotics in the Department of Media, Communications, Creative Arts, Language and Literature, and a professional saxophonist, clarinettist and flautist.
"It listens, it plays, and behaves just like a collaborating musician on stage, responding to the human members of the band," Savery says.
"It's all AI underneath and a whole bunch of different deep learning elements.
"And while I don't have to pay Keirzo – robots cost a lot to make and maintain and train. Everything is an expensive process."
From The Lighthouse, MacQuarie University (Australia)
Considering Fact Checking on Google Search
Its Fact Checking Day!
The topic comes up again, How do we do this well? Oddly though not a thing about 'Bard' where it has come up too.
Five new ways to verify info with Google Search Mar 28, 2023
Itamar Snir, Product Manager, Google News
Nidhi Hebbar, Product Manager
This illustration shows a Google "g" in the middle of different visualization of features people could see across our surfaces
People come to Google to find information quickly, understand complex topics and to parse facts from fiction. Google Search helps people find high-quality information from around the world, from a variety of diverse and credible sources, and find new perspectives to help them better understand the world. But sifting through all of the content available on the web can feel overwhelming, especially when you're trying to determine what information you can rely on.
International Fact-Checking Day on April 2 is a call to action – because in this day and age, nearly everyone has found themselves needing to put on their own fact-checking hat to verify a claim or check a source. Everyone should be empowered with the tools they need to find information they can trust. That’s why we build features to help you evaluate the information you come across online while expanding the range of helpful information you can find. Today we’re sharing how we’re expanding and improving these resources on Google Search.
Learn more with About this result – now available globally
When you search on Google, you probably see results from a number of websites and sources that you recognize – maybe it’s your favorite store or a blog you read regularly. But there also might be sites or sources that you haven’t come across before or aren’t as familiar with. To help people evaluate information and understand where it’s coming from, our About this result feature will be available in all languages where Search is available in the coming days. Now, wherever you’re searching, you’ll see three dots next to most results on Google Search. Tapping those three dots gives you a way to learn more about where the information you’re seeing is coming from and how our systems determined it would be useful for your query. With this context, you can make a more informed decision about the sites you may want to visit and what results will be most helpful for you. ... '
AI Regulation Examined
Looking at how AI may be regulated.
AI News
What will AI regulation look like for businesses?
Unlike food, medicine, and cars, we have yet to see clear regulations or laws to guide AI design in the US. Without standard guidelines, companies that design and develop ML models have historically worked off of their own perceptions of right and wrong.
This is about to change.
As the EU finalizes its AI Act and generative AI continues to rapidly evolve, we will see the artificial intelligence regulatory landscape shift from general, suggested frameworks to more permanent laws.
The EU AI Act has spurred significant conversations among business leaders: How can we prepare for stricter AI regulations? Should I proactively design AI that meets this criterion? How soon will it be before similar regulation is passed in the US?
Continue reading to better understand what AI regulation may look like for companies in the near future.
How the EU AI Act will impact your business
Like the EU’s General Data Protection Regulation (GDPR) released in 2018, the EU AI Act is expected to become a global standard for AI regulation. Parliament is scheduled to vote on the draft by the end of March 2023, and if this timeline is met, the final AI Act could be adopted by the end of the year.
It’s highly predicted that the effects of the AI Act will be felt beyond the EU’s borders (read: Brussels effect), albeit it being European regulation. Organizations operating on an international scale will be required to directly conform to the legislation. Meanwhile, US and other independently-led companies will quickly realize that it’s in their best interest to comply with this regulation.
We’re beginning to see this already with other similar legislation like Canada’s Artificial Intelligence & Data Act proposal and New York City’s automated employment regulation.
AI system risk categories
Under the AI Act, organizations’ AI systems will be classified into three risk categories, each with their own set of guidelines and consequences.
Unacceptable risk. AI systems that meet this level will be banned. This includes manipulative systems that cause harm, real-time biometric identification systems used in public spaces for law enforcement, and all forms of social scoring.
High risk. These AI systems include tools like job applicant scanning models and will be subject to specific legal requirements.
Limited and minimal risk. This category encompasses many of the AI applications businesses use today, including chatbots and AI-powered inventory management tools, and will largely be left unregulated. Customer-facing limited-risk applications, however, will require disclosure that AI is being used.
What will AI regulation look like?
Because the AI Act is still under draft, and its global effects are to be determined, we can’t say with certainty what regulation will look like for organizations. However, we do know that it will vary based on industry, the type of model you’re designing, and the risk category in which it falls.
Regulation will likely include scrutiny with a third party, where your model is stress tested against the population you’re attempting to serve. These tests will evaluate questions including ‘Is the model performing within acceptable margins of error?’ and ‘Are you disclosing the nature and use of your model? ‘
For organizations with high-risk AI systems, the AI Act has already outlined several requirements:
Implementation of a risk-management system.
Data governance and management.
Technical documentation.
Record keeping and logging.
Transparency and provision of information to users.
Human oversight.
Accuracy, robustness, and cybersecurity.
Conformity assessment.
Registration with the EU-member-state government.
Post-market monitoring system.
We can also expect regular reliability testing for models (similar to e-checks for cars) to become a more widespread service in the AI industry. ..... '
Book: In the Age of AI, 'What Does it Mean to be Smart?'
Here an intro to McKinsey's 'Author Talks', which interviews Tomas Chamorro-Premuzic on his new book on AI and Automation. Plan to Read it.
Author Talks: In the ‘age of AI,’ what does it mean to be smart?
March 16, 2023 | Interview
As artificial intelligence gets better at predicting human behavior, a business psychologist encourages people to strengthen the uniquely human skills that machine learning has yet to tap.
In this edition of Author Talks, McKinsey Global Publishing’s Raju Narisetti chats with Tomas Chamorro-Premuzic about his new book, I, Human: AI, Automation, and the Quest to Reclaim What Makes Us Unique (Harvard Business Review Press, February 2023). Chamorro-Premuzic explains why some AI algorithms model humanity as a simple species, how attention has become commoditized, and why the right questions are now more valuable than the right answers. An edited version of the conversation follows.
Why did you write this, your 12th book, now?
I’m a professor of business psychology at Columbia University and UCL [University College London] and the chief innovation officer at ManpowerGroup. I, Human: AI, Automation, and the Quest to Reclaim What Makes Us Unique is a book about the behavioral consequences or impact of artificial intelligence, including the dark side of human behavior and what we should do to upgrade ourselves as a species.
The book is written at a time that, in my view, could only be described as the AI age. Humans have always relied on technological inventiveness and innovation to shape their cultural and social evolution, and I think there can be very little doubt that the definitive technology of today is artificial intelligence, or AI.
Now, even the wider public is talking about things like ChatGPT and other conversational interfaces, and the tech giants are described mostly as data companies and as algorithmic prediction businesses.
The book was very much written in the midst of the AI age, or under the influence of AI, because I wrote the bulk of this at the height of the pandemic when we had very little physical interaction or contact with other people outside of our nuclear families. This means I was heavily influenced by hyperconnectedness and the datafication of me. Everything I did was being datafied and subjected to the predictive powers of AI during 2020 and 2021.
I can’t say that there won’t be a better era to read the book, but it certainly wouldn’t have had the same connotation and impact if we had published it five or ten years ago.
Haven’t humans always blamed technology for every problem they face?
There is a common tendency for people to overreact to things that are novel, whether in a good way or in a bad way, and technologies are a very good example of this.
Perhaps the best example is how, when the written newspaper first scaled up and productized, people feared that humans would never meet in person ever again because there would be no information or even gossip to exchange if all the news was in written form. Also, from the 1950s onward, people showed concern that television would lead to less intellectual activities, but I don’t think they were wrong because reading habits went down since mass TV was introduced.
What I tried to do with this book is not be at one extreme or the other. What’s important to me is to not miss the opportunity to highlight the behavioral impact and consequences that we have already seen artificial intelligence have on us. This is not a book about AI, but about humans in the AI age. .. .. '
Saturday, April 01, 2023
AI Addressing Dangerous Solar Storms
Am a long time follower of this very dangerous and inevitable natural event.
Deep Learning to Address Impact of Solar Storms.
NASA AI model could help world prepare for impact of solar storms
DAGGER artificial intelligence model uses NASA data to produce swift predictions, within 30 minutes of detection.
By Julia Musto https://www.nasa.gov/feature/goddard/2023/sun/nasa-enabled-ai-predictions-may-give-time-to-prepare-for-solar-storms
NASA said Thursday that a new computer model that combines artificial intelligence and agency satellite data could help prepare for dangerous space weather.
The model, called DAGGER (Deep Learning Geomagnetic Perturbation), uses the technical tool to analyze spacecraft measurements of the solar wind and forecast where an impending solar storm will strike on Earth – with 30 minutes of advance warning.
An international team of researchers at the Frontier Development Lab said the model can produce predictions in less than a second, with predictions updating every minute.
The lab is a partnership that includes NASA, the U.S. Geological Survey and the Department of Energy.
NASA's Solar Dynamics Observatory captured this image of a solar flare on Oct. 2, 2014. The solar flare is the bright flash of light at top. A burst of solar material erupting out into space can be seen just to the right of it.
NASA's Solar Dynamics Observatory captured this image of a solar flare on Oct. 2, 2014. The solar flare is the bright flash of light at top. A burst of solar material erupting out into space can be seen just to the right of it. (Credits: NASA/SDO)
The scientists had used A.I. to look for links between the solar wind and geomagnetic interruptions, applying a method called "deep learning" that trains computers to recognize patterns based on previous examples.
The model was tested against previous geomagnetic storms from August 2011 and March 2015, with DAGGER accurately forecasting the storm's impacts. Previously, models had used A.I. to forecast for specific locations, but NASA said DAGGER is the first to combine A.I. with real measurements to generate frequent and precise predictions worldwide.
"With this AI, it is now possible to make rapid and accurate global predictions and inform decisions in the event of a solar storm, thereby minimizing – or even preventing – devastation to modern society," Vishal Upendran of the Inter-University Center for Astronomy and Astrophysics in India, who is the lead author of a paper about the DAGGER model published in the journal Space Weather, said in a statement. ... ' (Images at the link) (plan to look at nature and goals of the model)
Made Me Think: About SynthAI
Still thinking this, Like to test its usefulness, efficiency. Join me.
For B2B Generative AI Apps, Is Less More? by Zeya Yang and Kristina Shen in Andreessen Horowitz
AI, machine & deep learning enterprise & SaaS Generative AI
Table of contents
Wave 1: Crossing the bridge from consumer to enterprise
What’s the cost (or benefit) of disrupting the workflow?
Wave 2: Converging information for improved decision making
Implementing SynthAI
A battle to own the workflow
We’ve watched large language models (LLMs) become mainstream over the past few years and have studied the implementations in the context of B2B applications. Despite some enormous technological advances and the presence of LLMs in the general zeitgeist, we believe we’re still only in the first wave of generative AI applications for B2B use cases. As companies nail down use cases and seek to build moats around their products, we expect a shift in approach and objectives from the current “Wave 1” to a more focused “Wave 2.”
Here’s what we mean: To date, generative AI applications have overwhelmingly focused on the divergence of information. That is, they create new content based on a set of instructions. In Wave 2, we believe we will see more applications of AI to converge information. That is, they will show us less content by synthesizing the information available. Aptly, we refer to Wave 2 as synthesis AI (“SynthAI”) to contrast with Wave 1. While Wave 1 has created some value at the application layer, we believe Wave 2 will bring a step function change.
Ultimately, as we explain below, the battle among B2B solutions will be less focused on dazzling AI capabilities, and more focused on how these capabilities will help companies own (or redefine) valuable enterprise workflows. ... ' (charts at the link at Andreessen)
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
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. ...
Pausing AI is a Bad idea. I agree.
Sure, we should allocate time to make it safer, and more useful. But that is already happening. Lets not let China and Russia get ahead. Unless they are pausing too?
Pausing AI is a Bad Idea. By SpencerAnte in FastCompany
The gloves are coming off in the fight over the future of AI.
On Tuesday, the Future of Life Institute, a futurist nonprofit backed by the Musk Foundation, published an open letter calling for a six-month pause on training AI systems more powerful than OpenAI’s leading GPT-4 service.
“Powerful AI systems should be developed only once we are confident that their effects will be positive and their risks will be manageable,” declares the letter, which has been signed by several thousand people, including Elon Musk himself, Apple cofounder Steve Wozniak, AI researchers Yoshua Bengio and Gary Marcus, and historian Yuval Noah Harari. “AI labs and independent experts should use this pause to jointly develop and implement a set of shared safety protocols for advanced AI design and development that are rigorously audited and overseen by independent outside experts.”
While there’s no doubt that AI should be developed in a way that is safe, responsible, and transparent, putting the most critical technology of our age in a timeout is an unviable solution that could weaken our country at a critical moment.
For starters, it would be an unprecedented move, coming just when AI is beginning to show incredible promise after decades of unfulfilled hype. It would also be nearly impossible to enforce and a gut punch to innovation—the engine of our economy.
While the letter has been signed by some notable AI experts, other AI researchers criticized the approach and said it overlooked harms and risks posed by current AI, like requiring more transparency of AI training data and decision-making of large language models. Computer scientist Andrew Ng, founder of Google Brain, called the moratorium “a terrible idea” on Twitter because government intervention would be the only possible way to enforce it.
“I’m seeing many new applications in education, healthcare, food, . . . that’ll help many people. Improving GPT-4 will help,” he tweeted. “Let’s balance the huge value AI is creating versus realistic risks. To advance AI safety, regulations around transparency and auditing would be more practical and make a bigger difference.”
Imagine people asking Netscape, Microsoft, and Mozilla to stop the development of the Web browser back in the mid-1990s. Would that have been the right move to address real concerns about online child pornography and indecent speech? Absolutely not. Those issues were more effectively addressed by industry, lawmakers, the courts, and regulators, ultimately being resolved through a landmark Supreme Court decision that enshrined the value of free speech on the internet.
Second, the U.S. is engaged in a competition with China to lead the AI market. Thanks to recent innovation of U.S.-based OpenAI, other U.S. multinationals like Microsoft, Google and Meta, and a bevy of startups, the U.S. may have retaken the lead in this race in which experts said China was ahead just a few years ago. But the pace of AI innovation is accelerating at a rate not seen since the boom of mobile computing. Consider that it took just under four months for OpenAI to release GPT-4 after its groundbreaking release of ChatGPT.
If the U.S. and its leading corporations paused AI development for six months while China raced ahead, it would put our country at a disadvantage and create a potential opening for our primary global adversary. Imagine if China’s AI leapfrogged the US during this pause, and the long term harm that could bring to democracy and geopolitical security. .... '
Meta Still Happening
Needs better design, communications, Sensible value, AI that really helps.
Meta Declares the Metaverse Is Still Happening, but It Might 'Take a While'
The company's head of global affairs recently doubled down on the company's commitment to creating the 'next big thing' in computing.
By Josh Norem March 31, 2023 in Extremetech
f anyone was wondering whether the company that changed its name to Meta recently was about to ditch the metaverse like other recent tech companies have, it's not. One of its top executives held a virtual press conference this week in -- where else? -- the company's metaverse to let reporters know it's still working on it.
The meeting featured Nick Clegg, head of global affairs for Meta, and various Washington D.C. reporters. All of the meeting's attendees appeared as avatars, and the reporters obviously had to borrow their headsets. Also, only Clegg's avatar looked like him, according to Bloomberg. With the avatars gathered around a large wooden table, Glegg declared the company was still all in on the metaverse. "We’re going to stick with it," he said, "because we really believe, all the early evidence suggests, that something like this will be the heart of the new computing platform.” However, he added, “But it’s going to take a while.” ... '
Venus Flytrap Cyborg Snaps Shut with Smartphone Commands
Venus Flytrap Cyborg Snaps Shut with Smartphone Commands By New Scientist March 29, 2023
The jaws of a Venus flytrap attached to a robotic arm.
Li says these new electrodes stay more securely attached to the plant than electrodes made of silver chloride that plant researchers typically use. They also have lower electrical resistance, so they are more energy-efficient.
Wenlong Li and colleagues at Singapore's Nanyang Technological University have transformed Venus flytraps into biological robots.
The researchers attached to the plant special electrodes made from a new type of hydrogel combined with a silver mesh conductor.
A wireless chip added to the electrodes allows the researchers to command the flytrap to shut its leaves via smartphone.
The team also detached and connected the plant's "jaws" to a robotic arm, enabling them to pick up thin platinum wire through wireless control.
The researchers say commanding flytraps to open after closing is more challenging, as the process takes up to an hour and cannot be sped up by electrical impulses.
From New Scientist
Google Bard had some issues, Plan is to try again
Google to try again with GPT
Google promises to unleash more of Bard’s potential in the 'next week'
By David Nield published ago
Like a "souped-up Civic"
Google Bard being used on a phone
Google Bard is currently available to test.
It feels as though Google is playing catch up at the moment when it comes to the ChatGPT-powered AI that Microsoft has introduced to Bing – but Google CEO Sundar Pichai says that his company's own Bard bot is going to quickly get more capable.
In an interview with the NYT's Hard Fork (opens in new tab) podcast (via The Verge(opens in new tab)), Pichai said that Bard was currently like a "souped-up Civic" taking on "more powerful cars" – but also that Google has "more capable models" that are going to get deployed in the coming days.
"We knew when we were putting Bard out we wanted to be careful," Pichai said. "Since this was the first time we were putting out, we wanted to see what type of queries we would get. We obviously positioned it carefully." .... '
VR for Therapy
Have seen some proposals for this, but no clear benefits indicated.
VR Is Revolutionizing Therapy. Why Aren't More People Using It? By CNet, March 27, 2023
High costs, VR's slow adoption, and a lack of awareness about VR therapy are to blame for its slow uptake.
The idea of using virtual reality to ease anxiety and overcome phobias isn't new; it's been studied since the 1990s.
Sam Stokes, a New Zealand-based sales manager, isn't usually an anxious person. But there's one thing that, as he puts it, scared the shit out of him: needles.
His aversion was severe enough to hold him back from getting routine tests. Stokes, now 40, recalls an instance in his 20s when he simply couldn't bring himself to get a blood test. He once even drove to the testing facility to get his blood drawn, but couldn't follow through with it. His partner (now wife) eventually convinced him to get the test, but he remembers it as one of "the most horrific" experiences he's had.
"I kind of passed out a little bit along the way, and was sweaty and clammy and all that sort of stuff," he said. "I just absolutely hated the whole experience."
When the COVID-19 pandemic arrived, he knew he couldn't let his needle phobia hold him back. Even watching the news became difficult, as stations regularly ran stories about vaccine developments.
From CNet
Friday, March 31, 2023
ChatGPT banned in Italy over privacy, data collection concerns
Unexpected Privacy concerns posed.
Calls have grown in the United States to stop development of the AI technology By Kelsey Koberg
DataGrade founder Joe Toscano and MRC Free Speech America VP Dan Schneider said ChatGPT poses various dangers to jobs and information.
Experts say biased data in ChatGPT could make AI ‘more dangerous,’ impact journalism
Italy’s privacy regulator ordered a ban Friday on ChatGPT over alleged privacy violations. ... '