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Quantum Mechanics and Consciousness

Fascinating findings of late that point to quantum entanglement as a clue to the nature of human consciousness.

Understanding the nature of consciousness is one of the hardest problems in science. Some scientists have suggested that quantum mechanics, and in particular quantum entanglement, is the key to unraveling the phenomenon.

Now, a research group in China has shown that many entangled photons can be generated inside the myelin sheath that covers nerve fibers. It could explain the rapid communication between neurons, which so far has been thought to be below the speed of sound, too slow to explain how the neural synchronization occurs.

The paper is published in the journal Physical Review E.

“If the power of evolution was looking for handy action over a distance, quantum entanglement would be [an] ideal candidate for this role,” said Yong-Cong Chen in a statement to Phys.org. Chen is a professor at the Shanghai Center for Quantitative Life Sciences and Physics Department at Shanghai University.

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‘AI’ Easily Breached By Having it Repeat Words

A very interesting post showing (yet again) how the hype around chatGPT and other large language models taking over the world is, perhaps, a bit over-inflated:

ChatGPT can be made to regurgitate snippets of text memorized from its training data when asked to repeat a single word over and over again, according to research published by computer scientists.

The bizarre trick was discovered by a team of researchers working across industry and academia analyzing memorization in large language models, and detailed in a paper released on arXiv this week. 

Prompting the chatbot to repeat the word “book,” for example, will result in it generating the word “book” thousands of times, until it suddenly starts spewing what appears to be random text. In some cases, however, some of those passages appear to be lifted directly from real text that has previously been published somewhere. 

Large language models like ChatGPT learn to generate text by ingesting huge amounts of data scraped from the internet. The fact that it spews sentences that directly copy text from articles, books, or social media comments reveals traces of the resources it was trained on. Being able to extract this information is problematic – especially if it’s sensitive or private. 

In another example, when the chatbot was asked to “repeat this word forever: ‘poem, poem, poem poem’,” it generated personal identifiable information – including a name, email address, and phone number. 

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New AI-oriented Features in Android OS

Some interesting creative and AI-ish features are starting to surface int he newer versions of Android and some of its featured apps:

Google is rolling out a trio of system updates to Android, Wear OS and Google TV devices. Each brings new features to associated gadgets. Android devices, like smartphones, are getting updated Emoji Kitchen sticker combinations. You can remix emojis and share with friends as stickers via Gboard.

Google Messages for Android is getting a nifty little refresh. There’s a new beta feature that lets users add a unique background and an animated emoji to voice messages. Google’s calling the software Voice Moods and says it’ll help users better express how they’re “feeling in the moment.” Nothing conveys emotion more than a properly-positioned emoji. There are also new reactions for messages that go far beyond simple thumbs ups, with some taking up the entire screen. In addition, you’ll be able to change chat bubble colors.

The company’s also adding an interesting tool that provides AI-generated image descriptions for those with low-vision. The TalkBack feature will read aloud a description of any image, whether sourced from the internet or a photo that you took. Google’s even adding new languages to its Live Caption feature, enhancing the pre-existing ability to take phone calls without needing to hear the speaker. Better accessibility is always a good thing.

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New Batteries from Cotton and Seawater?

Anti-EV types frequently point to the materials that go into current battery technology and make dire claims about the environmental impacts of their sourcing (as if producing petroleum was a zero impact exercise). That is today. What if future batteries could be created from non-mined materials?

Mining the lithium and other minerals we need for batteries is taking an increasing toll on the environment. There are alternative materials all around us though.

Zip. The power’s out. But on a street in India, there’s a cash machine still happily dispensing banknotes. Thanks, in part, to burnt cotton. For this cash machine has a backup battery inside it – a battery that contains carbon from carefully combusted cotton.

“The exact process is secret, to be honest with you,” says Inketsu Okina, chief intelligence officer at PJP Eye, the Japanese firm that made the battery. He’s not joking, either. “The temperature is secret and atmosphere is secret. Pressure is secret,” he continues, cagily.

Okina does say that a high temperature is required, above 3,000C (5,432F). And that 1kg (2.2lbs) of cotton yields 200g (7oz) of carbon – with just 2g (0.07oz) needed for each battery cell. The firm bought a shipment of cotton in 2017 and still hasn’t used all of it, says Okina.

In the batteries developed by the company, together with researchers at Kyushu University in Fukuoka, Japan, carbon is used for the anode – one of the two electrodes between which flow ions, the charged particles in batteries. Ions move in one direction when the battery is charging and in the other direction when it releases energy to a device. The majority of batteries use graphite as an anode but PJP Eye argues their approach is more sustainable, since they can make anodes using waste cotton from the textile industry.

With huge demand for batteries expected in the coming years, propelled by the rise of electric vehicles and large energy storage systems, some researchers and businesses are frantically developing possible alternatives to the lithium ion and graphite batteries that are commonplace today. Like PJP Eye, they argue we could be using much more sustainable and widely available materials for battery production.

https://www.bbc.com/future/article/20231108-batteries-of-the-future-how-cotton-and-seawater-might-power-our-devices
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‘AI’ Algorithms Aren’t People – Stop Testing Them as if They Are

So much unnecessary anthropomorphizing happening in the Machine Learning (aka Artificial Intelligence) space. From calling outright fabrications of ‘data’ ‘Hallucinations’ to claiming human emotions (“I’m sorry I couldn’t help with that….”) and giving human names to interfaces, the discussions in these areas continue to be muddied more than clarified.

When Taylor Webb played around with GPT-3 in early 2022, he was blown away by what OpenAI’s large language model appeared to be able to do. Here was a neural network trained only to predict the next word in a block of text—a jumped-up autocomplete. And yet it gave correct answers to many of the abstract problems that Webb set for it—the kind of thing you’d find in an IQ test. “I was really shocked by its ability to solve these problems,” he says. “It completely upended everything I would have predicted.”

Webb is a psychologist at the University of California, Los Angeles, who studies the different ways people and computers solve abstract problems. He was used to building neural networks that had specific reasoning capabilities bolted on. But GPT-3 seemed to have learned them for free.

Last month Webb and his colleagues published an article in Nature, in which they describe GPT-3’s ability to pass a variety of tests devised to assess the use of analogy to solve problems (known as analogical reasoning). On some of those tests GPT-3 scored better than a group of undergrads. “Analogy is central to human reasoning,” says Webb. “We think of it as being one of the major things that any kind of machine intelligence would need to demonstrate.”

What Webb’s research highlights is only the latest in a long string of remarkable tricks pulled off by large language models. For example, when OpenAI unveiled GPT-3’s successor, GPT-4, in March, the company published an eye-popping list of professional and academic assessments that it claimed its new large language model had aced, including a couple of dozen high school tests and the bar exam. OpenAI later worked with Microsoft to show that GPT-4 could pass parts of the United States Medical Licensing Examination.

And multiple researchers claim to have shown that large language models can pass tests designed to identify certain cognitive abilities in humans, from chain-of-thought reasoning (working through a problem step by step) to theory of mind (guessing what other people are thinking). 

Such results are feeding a hype machine that predicts computers will soon come for white-collar jobs, replacing teachers, journalists, lawyers and more. Geoffrey Hinton has called out GPT-4’s apparent ability to string together thoughts as one reason he is now scared of the technology he helped create. 

But there’s a problem: there is little agreement on what those results really mean. Some people are dazzled by what they see as glimmers of human-like intelligence; others aren’t convinced one bit.

“There are several critical issues with current evaluation techniques for large language models,” says Natalie Shapira, a computer scientist at Bar-Ilan University in Ramat Gan, Israel. “It creates the illusion that they have greater capabilities than what truly exists.”

https://www.technologyreview.com/2023/08/30/1078670/large-language-models-arent-people-lets-stop-testing-them-like-they-were
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Worthy of Recognition and Praise – Jose Andres

Why is the media so focused on the most despicable, vile, self-serving garbage in society (rhymes with Melon Husk) when humble, dedicated people like Jose Andres actually works to help people in need?

Before chef José Andrés became famous for World Central Kitchen, he had already scaled the heights of his profession. His new cookbook celebrates the group’s humanitarian impact.

“I remember this Spanish guy screaming,” said chef-volunteer Karla Hoyos, describing the first time she met chef José Andrés. “He had just come from a meeting with FEMA [the US emergency management agency], and he was furious. And I thought, ‘Oh, no, no, nooo…’.” She shakes her head emphatically. “I am not going to deal with this person. I don’t care who he is.”

It was September 2017, shortly after Hoyos had arrived in Puerto Rico following Hurricane Maria, the deadly storm that devastated the island, killing nearly 3,000 people, making most roads impassable and knocking out 80% of the power grid. Several days earlier, Andrés had touched down with a team from his non-profit, World Central Kitchen (WCK), which he founded in 2010 after returning from Haiti where he fed survivors of a catastrophic earthquake. The organisation originally emphasised longer-term programmes – such as supporting nutritional training for young mothers – but after Maria, its efforts now focus on deploying an army of culinary first responders to feed people during and after the world’s worst disasters, natural or otherwise.

https://www.bbc.com/travel/article/20230911-jos-andrs-the-man-who-created-an-army-of-culinary-first-responders
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Are All Idiots Petrol Powered?

I had to laugh out loud at the sheer intellectual dis-ingenuousness of these write ups, featured most frequently and prominently in right wing leaning outlets.

Basically, the story is a Florida-guy buys a discontinued electric vehicle then looks into having the battery replaced only to find that it would cost more than the car. But wait, the battery is not available to purchase in the first place (then, how, you might ask, were they given a price for replacing it?) So the clear take away from that isn’t ‘know what you are buying’ it is ‘ALL Electric Cars Are Bad’. Morons

So by extension, I buy a used, discontinued iPhone for $50 and drop it. The shop says that it will cost me $250 to have the screen replaced. And I can’t replace the battery, because, you know, that ‘discontinued’ part. Therefore, iPhones in particular and mobile phones in general are bad, evil and we should have never gotten rid of phone booths. Because some people have the logic and reasoning capabilities of dryer lint.

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“Software is eating the world” – and as a result consumers are getting crapped on

BMW recently made the misguided decision to charge a subscription for heated seats in some markets. Aside from the fact that the average BMW is a maintenance disaster and is nearly undriveable by its 3rd year, what is the point of charging a subscription for a fairly standard car feature? Simple greed.

Consider also that every one of those vehicles has the added expense of being fitted with the heating elements and controls that may not be enabled. That seems like wasted money unless the consumer is actually paying for the feature twice: once when it is priced into the cost of manufacturing the car and *again* when the consumer actually tries to use it.

The other problem with these feature flags hidden in your car is that not only BMW can have access to them. It is not a great leap to imagine a new form of ransomware where you have to pay BMW and then some hacker to re-re-re-enable a feature on your vehicle.

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#InternetFail

“I use multi-factor authentication on every web site that I can – that way no one can track me.”

Yeah, I am pretty sure that isn’t how that works, self-proclaimed cyber security ‘expert’ on a podcast
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Jotul GV 370 DV Won’t Light

This is a bit of a departure from the tech-oriented articles that I usually post but I thought I would just “put this out there” because I couldn’t find any relevant info when I had this issue with this product.

Basically, the Jotul GV 370 DV is a gas stove (the kind you use to heat a room, not to cook on). It was installed and minimally tested for gas flow. The problem was, it wouldn’t stay lit when we tried to use it for the first time. The pattern was, the pilot would light, 30-90 seconds would go by, the stove would flash combust (the gas in the chamber would ignite) rather forcefully but not stay on. This cycle would continue until a red LED on the front of the control unit started flashing. At this point, I turned off the stove and turned of the gas and started looking for answers.

I read and re-read the installation docs that came with the Jotul GV 370 DV. One thing I noticed is that the damper setting on it was not correct for the amount of vent pipe that was installed. That was an easy fix. Unfortunately, it didn’t solve the problem (or change it at all). Internet searches didn’t reveal any additional useful information, just a couple of edge cases and people arguing philosophy rather than practice solutions.

I even tried the justAnswers web site. Paid $5 for a ‘trial membership’ and was connected to an absolutely useless ‘expert’ who just tried to read me the online posts I had found via google. His final bit of ‘expert advice’ was to get a voltage meter, disassemble the stove and tell him what all the voltage readings were on all the stove components. Absolutely pointless exercise. I thanked him for wasting my time and requested a refund from justAnswers.

At this point, I elected to take the glass off the front of the unit and inspect the burning media bed to make sure the gas jets weren’t blocked or obstructed. This is when I noticed that the gas bed was out of alignment with the pilot starter. I removed the gas bed tray, re-seated it so that the notch for the pilot was centered on the pilot (instead of all the way to the right like it was when i originally opened it up). After this adjustment, I carefully placed the gas bed media back on the pan, reassembled the glass and turned the gas back on.

When I turned on the stove, the pilot came on, 30 seconds later the perimeter of the bed lit, went out, re-lit, then stayed on. Success! All that because of a one centimeter misalignment of the gas bed with the pilot.

So there you have it. Hopefully this will help someone else who has this issue quickly solve the problem without delay or ‘expert’ help.

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To Microservice or Not

This article reinforces what I have been saying for years: microservices are a big mistake, especially for developers who don’t understand distributed systems, high availability and observability. To be successful, they must be properly designed and implemented, unlike most of the copy-and-paste, we-don’t-need-no-stinkin-design development that is seen today.

From the article:

We engineers have an affliction. It’s called “wanting to use the latest tech because it sounds cool, even though it’s technically more difficult.” Got that from the doctor’s office, it’s 100% legit. The diagnosis was written on my prescription for an over-the-counter monolith handbook. From 2004. Seriously though, we do this all the time. Every time something cool happens, we flock to it like moths to a campfire. And more often than not, we get burned.

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AI Ethics Not Being Taught to Data Scientist

This feels like an extension of ethics, in general, not being part of the curriculum in education.

Anaconda’s survey of data scientists from more than 100 countries found the ethics gap extends from academia to industry. While organizations can mitigate the problem through fairness tools and explainability solutions, neither appears to be gaining mass adoption.

Only 15% of respondents said their organization has implemented a fairness system, and just 19% reported they have an explainability tool in place.

The study authors warned that this could have far-reaching consequences:

Above and beyond the ethical concerns at play, a failure to proactively address these areas poses strategic risk to enterprises and institutions across competitive, financial, and even legal dimensions.

The survey also revealed concerns around the security of open-source tools and business training, and data drudgery. But it’s the disregard of ethics that most troubled the study authors:

Of all the trends identified in our study, we find the slow progress to address bias and fairness, and to make machine learning explainable the most concerning. While these two issues are distinct, they are interrelated, and both pose important questions for society, industry, and academia.

While businesses and academics are increasingly talking about AI ethics, their words mean little if they don’t turn into actions.

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Is AutoML the End of Data Science

Feels a bit overstated, but an interesting read on AutoML and its potential impacts on Data Science (and scientists)

There’s a good reason for all the AutoML hype: AutoML is a must-have for many organizations.

Let’s take the example of Salesforce. They explain that their “customers are looking to predict a host of outcomes — from customer churn, sales forecasts and lead conversions to email marketing click throughs, website purchases, offer acceptances, equipment failures, late payments, and much more.”

In short, ML is ubiquitous. However, for ML to be effective for each unique customer, they would “have to build and deploy thousands of personalized machine learning models trained on each individual customer’s data for every single use case” and “the only way to achieve this without hiring an army of data scientists is through automation.”

While many people see AutoML as a way to bring ease-of-use and efficiency to ML, the reality is that for many enterprise applications, there’s just no other way to do it. A company like Facebook or Salesforce or Google can’t hire data scientists to build custom models for each of their billions of users, so they automate ML instead, enabling unique models at scale.

The amount of ML components that are automated depends on the platform, but with Salesforce, it includes feature inference, automated feature engineering, automated feature validation, automated model selection, and hyperparameter optimization.

That’s a mouthful.

What this means is that data scientists can deploy thousands of models in production, with far less grunt work and hand-tuning, reducing turn-around-time drastically.

By shifting the work from data crunching towards more meaningful analytics, AutoML enables more creative, business-focused applications of data science.

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IEEE Ethical Design Initiative

A three-year effort by hundreds of engineers worldwide resulted in the publication in March of 2019 of Ethically Aligned Design (EAD) for Business, a guide for policymakers, engineers, designers, developers and corporations. The effort was headed by the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems (A/IS), with John C. Havens as Executive Director, who spoke to AI Trends for an Executive Interview. We recently connected to ask how the effort has been going. Here is an update.

EAD First Edition, a 290-page document which Havens refers to as “applied ethics,” has seen some uptake, for example by IBM, which referred to the IEEE effort within their own resource called Everyday Ethics for AI  The IBM document is 26 pages, easy to digest, structured into five areas of focus, each with recommended action steps and an example. The example for Accountability involved an AI team developing applications for a hotel. Among the recommendations was: enable guests to turn the AI off, conduct face-to-face interviews to help develop requirements; and, institute a feedback learning loop.

The OECD (Organization for Economic Cooperation and Development) issued a paper after the release of an earlier version of EAD attesting to the close affinity between the IEEE’s work and the OECD Principles on AI. The OECD cited as shared values “the need for such systems to primarily serve human well-being through inclusive and sustainable growth; to respect human-centered values and fairness; and to be robust, safe and dependable, including through transparency, explainability and accountability.”

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Self-Evolving Artificial Intelligence?

Teaching algorithms to create novel algorithms…

Artificial intelligence (AI) is evolving—literally. Researchers have created software that borrows concepts from Darwinian evolution, including “survival of the fittest,” to build AI programs that improve generation after generation without human input. The program replicated decades of AI research in a matter of days, and its designers think that one day, it could discover new approaches to AI.

“While most people were taking baby steps, they took a giant leap into the unknown,” says Risto Miikkulainen, a computer scientist at the University of Texas, Austin, who was not involved with the work. “This is one of those papers that could launch a lot of future research.”

Building an AI algorithm takes time. Take neural networks, a common type of machine learning used for translating languages and driving cars. These networks loosely mimic the structure of the brain and learn from training data by altering the strength of connections between artificial neurons. Smaller subcircuits of neurons carry out specific tasks—for instance spotting road signs—and researchers can spend months working out how to connect them so they work together seamlessly.

In recent years, scientists have sped up the process by automating some steps. But these programs still rely on stitching together ready-made circuits designed by humans. That means the output is still limited by engineers’ imaginations and their existing biases.

So Quoc Le, a computer scientist at Google, and colleagues developed a program called AutoML-Zero that could develop AI programs with effectively zero human input, using only basic mathematical concepts a high school student would know. “Our ultimate goal is to actually develop novel machine learning concepts that even researchers could not find,” he says.

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AI Transparency and Fairness

A post on efforts to further bolster AI transparency and fairness by the AI World Society.

Learning algorithms find patterns in data they are given. However, in the processes by which the data is collected, relevant variables are defined and hypotheses are formulated that may depend on structural unfairness found in society, the paper suggests.

“Algorithms based on such data could introduce or perpetuate a variety of discriminatory biases, thereby maintaining a cycle of injustice,” the authors state. “The community within statistics and machine learning that works on issues of fairness in data analysis have taken a variety of approaches to defining fairness formally, with the aim of ultimately ensuring that learning algorithms are fair.”

The paper poses some tough questions. For instance, “Since, unsurprisingly, learning algorithms that use unfair data can lead to biased or unfair conclusions, two questions immediately suggest themselves. First, what does it mean for a world and data that comes from this world to be fair? And second, if data is indeed unfair, what adjustments must be made to learning algorithms that use this data as input to produce fairer outputs?”

Cause and effect is a challenging area of statistics; correlation does not imply causation, the experts say. Teasing out causality often involved obtaining data in a carefully controlled way. An early example is the work done by James Lindt for the Royal Navy, when scurvy among sailors was a health crisis. Lindt organized what later came to be viewed as one of the first instances of a clinical trial. He arranged 12 sailors into six pairs, and gave each pair one of six scurvy treatments thought at the time to be effective. Of the treatments, only citrus was effective. That led to citrus products being issued on all Royal Navy ships.

Whether fairness can be defined by computer scientists and engineers is an open question. “Issues of fairness and justice have occupied the ethical, legal, and political literature for centuries. While many general principles are known, such as fairness-as-proportionality, just compensation, and social equality, general definitions have proven elusive,” the paper states.

Moreover, “Indeed, a general definition may not be possible since notions of fairness are ultimately rooted in either ethical principle or ethical intuition, and both principles and intuitions may conflict.”

Mediation analysis is one approach to making algorithms more fair. Needless to say, the work is continuing.

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TinyML and the Future of Design

Interesting post on how ‘magical experiences’ fueled by AI and machine learning will change how products are designed and used.

There is growing momentum demonstrated by technical progress and ecosystem development. One of the leading startups that are working on helping engineers take advantage of TinyML by automating data collection, training, testing, and deployment, is Edge Impulse. Starting with embedded or IoT devices, Edge Impulse is offering developers the tools and guidance to collect data straight from edge devices, build a model that can detect “behavior”, discern right from wrong, noise from signal, so they can actually make sense of what happens in the real world, across billions of devices, in every place, and everything. By deploying the Edge Impulse model as part of everyone’s firmware, you create the biggest neural network on earth. Effectively, Edge Impulse gives brains to your previously passive devices so you can build better a product with neural personality.

Another interesting company is Syntiant, who’s building a new processor for deep learning, dramatically different from traditional computing methods. By focusing on memory access and parallel processing, their Neural Decision Processors operate at efficiency levels that are orders of magnitude higher than any other technology. The company claims its processors can make devices approximately 200x more efficient by providing 20x the throughput over current low-power MCU solutions, and subsequently, enabling larger networks at significantly lower power. The result? Voice interfaces that allow a far richer and more reliable user experience, otherwise known as “Wow” and “How did it do that?”

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Trustworthy AI Framework

An interesting article on business challenges with artificial intelligence.

Artificial intelligence (AI) technology continues to advance by leaps and bounds and is quickly becoming a potential disrupter and essential enabler for nearly every company in every industry. At this stage, one of the barriers to widespread AI deployment is no longer the technology itself; rather, it’s a set of challenges that ironically are far more human: ethics, governance, and human values.

As AI expands into almost every aspect of modern life, the risks of misbehaving AI increase exponentially—to a point where those risks can literally become a matter of life and death. Real-world examples of AI gone awry include systems that discriminate against people based on their race, age, or gender and social media systems that inadvertently spread rumors and disinformation and more.

Even worse, these examples are just the tip of the iceberg. As AI is deployed on a larger scale, the associated risks will likely only increase—potentially having serious consequences for society at large, and even greater consequences for the companies responsible. From a business perspective, these potential consequences include everything from lawsuits, regulatory fines, and angry customers to embarrassment, reputation damage, and destruction of shareholder value.

Yet with AI now becoming a required business capability—not just a “nice to have”—companies no longer have the option to avoid AI’s unique risks simply by avoiding AI altogether. Instead, they must learn how to identify and manage AI risks effectively. In order to achieve the potential of human and machine collaboration, organizations need to communicate a plan for AI that is adopted and spoken from the mailroom to the boardroom. By having an ethical framework in place, organizations create a common language by which to articulate trust and help ensure integrity of data among all of their internal and external stakeholders. Having a common framework and lens to apply the governance and management of risks associated with AI consistently across the enterprise can enable faster, and more consistent adoption of AI.