AI Godfather says New Tech Could be Dangerous

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

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

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

Andrea VacchianoBy Andrea Vacchiano | Fox News

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

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

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

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

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

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

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

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

Gordon Moore, Intel co-founder and creator of Moore's Law, dies aged 94

Gordon Moore, Intel co-founder and creator of Moore's Law, dies aged 94

Published, 14 hours ago  in the BBC. 

Silicon Valley pioneer and philanthropist Gordon Moore has died aged 94 in Hawaii.

Mr Moore started working on semiconductors in the 1950s and co-founded the Intel Corporation.

He famously predicted that computer processing powers would double every year - later revised to every two - an insight known as Moore's Law.

That "law" became the bedrock for the computer processor industry and influenced the PC revolution.

Two decades before the computer revolution began, Moore wrote in a paper that integrated circuits would lead "to such wonders as home computers - or at least terminals connected to a central computer - automatic controls for automobiles, and personal portable communications equipment".

He observed, in the 1965 article, that thanks to technological improvements the number of transistors on microchips had roughly doubled every year since integrated circuits were invented a few years earlier.

His prediction that this would continue became known as Moore's Law, and it helped push chipmakers to target their research to make this come true.

After Moore's article was published, memory chips became more efficient and less expensive at an exponential rate. ...   '

Computing and Dentistry

 My impression was there was relatively little done in this space, I was wrong.  Note use of CAD/CAM tools.  Here are some examples.

Computers Aid Dentistry, January 5, 2023

A digital representation of a tooth.

New small-scale in-office CAD/CAM tools make it possible to go from preparing the tooth to fixing the permanent crown in a single visit.

Americans get fitted with 2.3 million dental crowns a year, according to the American College of Prosthodontists. Anyone who has had one knows the fitting of a dental crown is usually a lengthy, multistep procedure.

The standard process (once the tooth is prepared) starts with the dentist taking an impression of the tooth using a horseshoe-shaped tray filled with a putty-like substance. The impression is sent off to a laboratory that makes a model of the tooth from the mold, scans the model, and manufactures a crown to fit. It can take two to three weeks for the permanent crown to come back from the lab, and during that time the patient has to wear a temporary version, usually a cruder tooth-sized "cap." Finally, the patient returns to the office to have the permanent crown placed and reshaped as necessary.

New small-scale in-office CAD/CAM tools make it possible to go from preparing the tooth to fixing the permanent crown in a single visit. These Chairside Economical Restoration of Esthetic Ceramics (CEREC) systems rely on handheld 3D scanners, specialized computer-aided design (CAD) software, milling machines, and sintering ovens. Using such a system, a dentist can take a scan of the tooth, have the software model the crown and modify it as desired, and manufacture the actual restoration all in a few hours, without involving an outside lab.

How it started

The idea behind CEREC systems arose around 1980, when Dr. Werner Mörmann, a Swiss prosthodontist, reasoned that inlays could be produced in the dentist office by grinding them out of a block of ceramic. By 1985, working with Dr. Marco Brandestini, an electrical engineer, Dr. Mörmann had developed the first CEREC system, which entered clinical trials in 1987.

It is difficult to get a fix on how many CEREC systems are in use today. In 2014, CEREC ACCEPT, which offers training in the use of CEREC systems, claimed that more than 40,000 systems had been sold worldwide. One of the reasons it's hard to pin down the number of systems in use is that a CEREC system comprises multiple components that don't necessarily all come from the same vendor. "Not every manufacturer provides all devices for a CEREC workflow," according to Dentsply Sirona, a manufacturer of CEREC systems and an early partner of Dr. Mörmann. "We know that some practices use more than one scanner or milling unit, for example."

According to a Fortune Business Insights report on the dental CAD/CAM market in 2021, "The market is highly fragmented," with just a few major players accounting for most of the global market and other vendors active in specific regions or market segments. The report identified Key Companies in the sector as including:

Align Technology, Inc. (U.S.)

Dentsply Sirona (U.S.)

Axsys Dental Solutions (U.S.)

Medit Corp. (South Korea)

Institut Straumann AG (Switzerland)

PLANMECA OY (Finland)

3Shape A/S (Denmark)

YENADENT (Turkey)  .... ' 

How Wide is Computing Divided?

 Short review extract:

Computing Divided: How Wide the Chasm?     By Andrew A. Chien

Communications of the ACM, January 2023, Vol. 66 No. 1, Page 5    10.1145/3572994

Andrew A. Chien, past Editor-in-Chief of CACM

In January 2019, I described the computing community on the cusp of a new era of growing division.2 The prognostication proved accurate. Four years have seen accelerating division and growing real fissures, driven by international geopolitics, dramatic spy cases—incarceration and increasing technology and trade restrictions. Examples include:

China's crackdown on tech and covert suppression of opinion abroad,1,4 and

China's threatened invasion of Taiwan and bullying of Lithuania.6

If you are not concerned yet, in February 2022 we saw an outbreak of a regional conflict akin to that which caused World War I;7 Russia invaded Ukraine with no justification. The invasion created overt alignment of warring alliances: China in a no-limits partnership with Russia, Iran, Belarus, and Syria, and the U.S., Europe, and the West joining to support Ukraine. China, the economic leader of Asia and major regional military power, spread a chill across Asia by siding with an imperialist "might makes right" alliance. All nations are now rethinking defense, investment, trade, and technology policies toward China.

The world is at an inflection point. These are indeed times for caution and careful reflection on where are we going. There are three major possibilities:

Catastrophe! (Future #1). Open hostility, fully separate trade (and travel and technology) partitions. Unthinkable? Recall this was the world order from 1950–1980 (Soviet Block—USSR and Eastern Europe, the Western Alliance—U.S and Europe, and China), disjointed ecosystems with little trade or travel. This isolation was self-imposed by both the Soviet Block and China, out of weakness to maintain control over their citizenry. Such division would be a terrible outcome. Tragic individually for billions of citizens of the closed systems, a painful quandary for those with ties spanning, and it would impoverish all culturally.

It is undeniable that Russia has gone into such isolation in just 12 months (trade and travel have stopped). China's no-limits alliance, actions against its tech companies, and overt international belligerence, suggest little commitment to a rules-based international order. Is China ready to undertake isolation? Unthinkable? Such isolation might make China's rulers feel more secure.  ... '  (more below at link)  

Opinion on the CHIPS Act

 Short Opinion piece from ACM site.  Have not reviewed further, but plan to

The False Promise of America's CHIPS Act

By Project Syndicate, November 28, 2022

The CHIPS Act is flawed in many ways.  (Opinion) 

The U.S. CHIPS and Science Act allocates more than $50 billion to strengthen the semiconductor industry in the hope of making the U.S. self-sufficient. But it is doubtful that the CHIPS Act will achieve its stated goals, much less that it should be used as a model for similar support to other industries.

While it is understandable that the U.S. wants to maintain its technological lead in chips, subsidizing large existing firms and government management of the industry almost certainly will not achieve that goal.

From Project Syndicate

View Full Article  

Re Ambient Computing

Anytime, anywhere,  data and computation delivered.

Why the Future of the Computer Is Everywhere, All the Time

By The Wall Street Journal

October 31, 2022

In the ambient world, the technology is all around us, unseeable and untouchable.

Imagine this scenario in the not-too-distant future. You're awakened at 6:11 a.m. by the gentle sounds of tinkling bells and birdsong, even though you live in a 12th-floor apartment. Your alarm clock uses radar to track your breathing, and wakes you gently, with sound and light, when it detects you're in a lighter phase of sleep.

Your transition to wakefulness triggers a cascade of changes in your apartment. Your window shades open automatically. In the kitchen, coffee starts brewing. As you pad into the bathroom to brush your teeth, a display projected onto the mirror above the sink shows your calendar for the day. It highlights what time you'll have to leave to get to your office for the in-person meeting you scheduled for 8:30.

Returning to your bedroom, you find your stowaway robotic bed has retracted into the ceiling, and your collapsible walk-in closet has expanded to reveal your clothes and a full-length mirror. The mirror suggests, based on your schedule and the weather, an outfit it displays as an augmented-reality overlay that moves with your body as you inspect yourself. You aren't fond of the first option, so you make a swipe-left gesture in the air. The mirror responds by suggesting another outfit. You signal assent, and the two drawers containing the items you want glow around their edges, so you don't have to waste time hunting for them.

From The Wall Street Journal

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Reports on Computing, Big and Small

Training and More

New Records for the Biggest and Smallest AI Computers Nvidia H100 and Intel Sapphire Rapids Xeon debut on ML Perf training benchmarks   By SAMUEL K. MOORE  in Spectrum IEEE 

The machine-learning consortium MLCommons released the latest set of benchmark results last week, offering a glimpse at the capabilities of new chips and old as they tackled executing lightweight AI on the tiniest systems and training neural networks at both server and supercomputer scales. The benchmark tests saw the debut of new chips from Intel and Nvidia as well as speed boosts from software improvements and predictions that new software will play a role in speeding the new chips in the years after their debut.

Training Servers

Training AI has been a problem that’s driven billions of dollars in investment, and it seems to be paying off. “A few years ago we were talking about training these networks in days or weeks, now we’re talking about minutes,” says Dave Salvator, director of product marketing at Nvidia.

There are eight benchmarks in the MLPerf training suite, but here I’m showing results from just two—image classification and natural-language processing—because although they don’t give a complete picture, they’re illustrative of what’s happening. Not every company puts up benchmark results every time; in the past, systems from Baidu, Google, Graphcore, and Qualcomm have made marks, but none of these were on the most recent list. And there are companies whose goal is to train the very biggest neural networks, such as Cerebras and SambaNova, that have never participated.

Another note about the results I’m showing—they are incomplete. To keep the eye glazing to a minimum, I’ve listed only the fastest system of each configuration. There were already four categories in the main “closed” contest: cloud (self-evident), on premises (systems you could buy and install in-house right now), preview (systems you can buy soon but not now), and R&D (interesting but odd, so I excluded them). I then listed the fastest training result for each category for each configuration—the number of accelerators in a computer. If you want to see the complete list, it’s at the MLCommons website.  .... ' 

A Silicon Image Sensor That Computes

New approach for image analysis?

A Silicon Image Sensor That Computes

Harvard University John A. Paulson School of Engineering and Applied Sciences

Leah Burrows, August 25, 2022

U.S. and South Korean researchers have developed in-sensor processors that could be incorporated into commercial complementary metal-oxide-semiconductor (CMOS) image sensors. The researchers created an electrostatically doped silicon photodiode array, enabling voltage-based tuning of pixels' optical sensitivity. Harvard University's Houk Jang said, "These dynamic photodiodes can concurrently filter images as they are captured, allowing for the first stage of vision processing to be moved from the microprocessor to the sensor itself." Users can program the array into different image filters to eliminate unnecessary details or noise for various applications. Harvard's Henry Hinton predicted the processor will see use "not only in machine vision applications, but also in bio-inspired applications, wherein early information processing allows for the co-location of sensor and compute units, like in the brain."

Quantifying Computing Power and Innovation

Powerful computers,  better innovation? 

Quantifying Computing Power and Innovation    By MIT News., June 29, 2022

Futuristic image of a microprocessor in a circuit.

"By looking directly at capabilities, we are able to get more precise measurements and thus get better estimates of how computing power influences performance." -Neil Thompson

Neil Thompson is an MIT research scientist at the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Sloan School of Management.

Neil Thompson and his research team set out to quantify the importance of more powerful computers for improving outcomes across society. They analyzed five areas where computation is critical, including weather forecasting, oil exploration, and protein folding. The team found that between 49 and 94 percent of improvements in these areas can be explained by computing power. But computer progress is slowing, which could have far-reaching impacts across the economy and society.

In an interview, Thompson discusses this research and the implications of the end of Moore's Law.

From MIT News

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Computing Speed Limit?

Interesting look, does it clearly include quantum? 

Speed Limit of Computers Detected

Graz University of Technology (Austria), Christoph Pelzl, March 25, 2022

At Germany's Ludwig Maximilian University of Munich and the Max Planck Institute of Quantum Optics and Austria's Vienna (TU Wien) and Graz Universities of Technology, researchers have determined that a microchip's maximum signal transmission speed is about 1 petahertz (1 million gigahertz), roughly 100,000 times faster than current transistors. The researchers focused on increasing the switching signals of transistors to increase the speed of data transmission. They applied an ultra-short laser pulse with frequency in the extreme UV range to a lithium fluoride sample and found that by putting the electrons in the lithium fluoride into a more energetic state, the material became an electrical conductor for a brief period. The excited electrons were steered in a desired direction with a second laser pulse that lasted slightly longer. TU Wien's Joachim Burgförder said, "At about 1 petahertz there is an upper limit for controlled optoelectronic processes."

HSBC Working with IBM for Quantum Computing Readiness

More collaborations to being Quantum forward. 

HSBC Working with IBM to Accelerate Quantum Computing Readiness

By IBM, March 30, 2022

  HSBC and IBM yesterday announced that they will work together on exploring applications for quantum computing in financial services.

The new three-year collaboration is designed to bolster HSBC's expertise in quantum computing and ensure its organizational readiness to take full advantage of the technology.

As part of the agreement, the bank will join the IBM Quantum Accelerator program, giving it access to IBM's premium plan of quantum computing systems, including its recently announced 127-qubit processor, Eagle, as well as IBM's quantum expertise, to help validate and progress potential quantum use cases.

HSBC will explore the use of quantum computing for pricing and portfolio optimization, to advance its net zero goals, and to mitigate risks, including identifying and addressing fraudulent activity.

The bank will upskill colleagues in quantum technology through internal training programs, as well as actively recruiting quantum computing research scientists, to build a dedicated capability within its innovation team.

From IBM  

More Laws of Computing

 Not so much laws, but rather general indications of past trends that may usefully hold in the future. or not.  Here some proposals for mew laws.Good for tracking against new data.  

Moore’s Not Enough: ​4 New laws of Computing Moore’s and Metcalfe’s conjectures are taught in classrooms every day—these four deserve consideration, too  by ADENEKAN DEDEKE in IEEE Spectrum.  Below an intro, much more at the link.

MOORE'S LAW METCALFE'S LAW COMPUTING

I teach technology and information-systems courses at Northeastern University, in Boston. The two most popular laws that we teach there—and, one presumes, in most other academic departments that offer these subjects—are Moore’s Law and Metcalfe’s Law. Moore’s Law, as everyone by now knows, predicts that the number of transistors on a chip will double every two years. One of the practical values of Intel cofounder Gordon Moore’s legendary law is that it enables managers and professionals to determine how long they should keep their computers. It also helps software developers to anticipate, broadly speaking, how much bigger their software releases should be.

Metcalfe’s Law is similar to Moore’s Law in that it also enables one to predict the direction of growth for a phenomenon. Based on the observations and analysis of Robert Metcalfe, co-inventor of the Ethernet and pioneering innovator in the early days of the Internet, he postulated that the value of a network would grow proportionately to the number of its users squared. A limitation of this law is that a network’s value is difficult to quantify. Furthermore, it is unclear that the growth rate of every network value changes quadratically at the power of two. Nevertheless, this law as well as Moore’s Law remain a centerpiece in both the IT industry and academic computer-science research. Both provide tremendous power to explain and predict behaviors of some seemingly incomprehensible systems and phenomena in the sometimes inscrutable information-technology world.

King Camp Gillette reduced the price of the razors, and the demand for razor blades increased. The history of IT contains numerous examples of this phenomenon, too.

I contend, moreover, that there are still other regularities in the field of computing that could also be formulated in a fashion similar to that of Moore’s and Metcalfe’s relationships. I would like to propose four such laws.

Law 1. Yule’s Law of Complementarity

I named this law after George Udny Yule (1912), who was the statistician who proposed the seminal equation for explaining the relationship between two attributes. I formulate this law as follows:   .... ' 

Global Computing Community Divided?

In the ACM some very useful thoughts.   How is global effort usefully shared?

Is the Global Computing Community Irrevocably Divided?  By Andrew A. Chien

Communications of the ACM, January 2022, Vol. 65 No. 1, Page 5 10.1145/3502906

We have all seen powerful forces pulling the global computing community apart.a With computing's critical importance as both a commercial and military technology, the community cannot avoid this tension.

Three years of overt technological, trade, military and geopolitical competition between China and the U.S. have been marked with trade war, spying, state-sponsored technology theft,b criminal warrants, export bans, territorial ocean claims, and incendiary national leadership rhetoric. Atop these is the stark undermining of Hong Kong's basic law, and the violent suppression of its free press, political opposition, and peaceful democratic protests in Hong Kong. Scenes of the violent assault of HK Polytechnic University and suppression of all HK opposition press have extinguished any hope China might evolve to tolerate diverse political thought and multiparty politics.c In China, the people in their entirety are subordinate to the Communist Party and government.

To drive home its absolute supremacy, in 2021, the PRC government undertook the systematic emasculation of tech-entrepreneur heroes, leading tech companies (Tencent, Alibaba, Meituan), and recently forced upon them controlling investments.d The clear messages in social media and official Chinese government channels are "no quarter given to opposition" and "foreign ties are no longer assets, but now liabilities."

The situation is not one-sided; the U.S. government has taken actions that discourage collaboration with Chinese institutions and researchers.e Companies have been put on the entity list, blocking contact, and universities labeled as having military ties, limiting their students' study in the U.S. With increased reporting of U.S. researcher affiliations, collaboration, or contacts, there is growing doubt about what is proper and trepidation about collaboration.

Is the global computing community irrevocably divided?

Further signs of division include Yahoo! China's closing, and Microsoft's decision to close LinkedIn in China, a key international professional social network.f The latter is particularly discouraging given Microsoft's long history of engagement with China's tech community through with MSR Asia in Beijing, long a taproot of the Chinese tech community.

Now stark, the growing division is technological, economic (companies and markets), and geopolitical (surveillance, cyber-attacks, and military capability). Corporate and government communities are rapidly codifying division and controlled information sharing.

What is the academic and research community to do?

What are the implications for widespread, open technical information sharing? Open source that has driven rapid scientific and technological advance.

What are the implications for professors and students (graduate and undergraduate) in both China and the U.S. whose ties and loyalties straddle these worlds?

What are the implications for Chinese students who study at universities around the world? Following a road trod by Japan, Taiwan, and Korea, a growing majority return to a career and their family in China.g These international students will be critical in bridging the divide.

An increasing schism may produce decreased collaboration and increasingly circumspect technical information (publications, talks, open source software) and technology (chips, open source software) sharing. With time, it will increase pressure on those with personal ties that straddle these two worlds. The divide has broad implications for the community on all seven continents, not just in the U.S. and China. The actions of these parties will be pivotal; neutrality is impossible, they must seriously consider where they stand and where it is worthwhile to put a thumb on the scales.  ... ' 

Principles for Innovative Engineers

Very useful principles below at the link, we worked with Rosalind Picard long ago ... 

What Every Engineer and Computer Scientist Should Know: The Biggest Contributor to Happiness

By Rosalind Picard     in CACM

Communications of the ACM, December 2021, Vol. 64 No. 12, Pages 40-42    10.1145/3465999

My teams at MIT and our spin-out companies have worked for years to create technology that is both intelligent and able to improve people's lives. Through research drawing from psychiatry, neuroscience, psychology, and affective computing, I have learned some surprising things. In some cases, they are principles we have embedded into technology that interacts with people. Guess what? People like it. After one year of the COVID-19 pandemic, I realize that the principles we learned apply not only to making smart robots or software agents, but also to the people around us. They give us lessons for how to live happier lives, and happier engineers are better at solving creative problems and have more fun.

Researchers have studied what brings happiness in life, and what, at the end of life, people wish they had done. While many factors contribute, do you know the biggest one?

Almost never late in life do people say: "I wish I had invented a smarter or faster device," "I wish I had made more money," "I wish I had given more TED talks," "I wish I had climbed higher in my business," or "I wish I had authored more books." Even this pinnacle of achievement is not uttered: "I wish I had written an article for an ACM magazine." Instead, almost always, people wish that they had done a better job at building meaningful authentic human relationships, and spending time in those relationships.

This finding is a general one, whether studying human happiness or end-of-life reflections. They apply to hard-working, well-educated computer scientists or engineers and also to many kinds of people, different races and cultures, rich and poor, male and female, uneducated or over-educated.

All of the patents, publications, presentations, and personal technical achievements can be amazing: They can literally save lives and bring immense delight, win us world acclaim, fill our shelves with awards, tally up clicks online, and even make our resumes impressively long. However, they all pale in comparison to something that is even more joy-giving: Achieving deeply satisfying, personally-significant human relationships.

How do you engineer great relationships? Here are three helpful principles you can test in your own life and relationships. If you build AI that interacts directly with people, you can build these principles into those interactions too. I learned these principles while trying to engineer more intelligence in machines, specifically computers with skills of social-emotional intelligence. The skills derive from studies of human relationships and they apply not only when the interactions involve two people, but also when one is a computer (including chatbots, software agents, robots, and other things programmed to talk with us). The three principles below can help improve relationships, human or AI.   ..... ....

(Full principles below)

Is AI Re-Inventing Computers?

Very interesting piece.  Anything can become a computer?

Three key ways artificial intelligence is changing what it means to compute.

By Will Douglas Heaven,  October 22, 2021  in TechnologyReview

Fall 2021: the season of pumpkins, pecan pies, and peachy new phones. Every year, right on cue, Apple, Samsung, Google, and others drop their latest releases. These fixtures in the consumer tech calendar no longer inspire the surprise and wonder of those heady early days. But behind all the marketing glitz, there’s something remarkable going on. 

Google’s latest offering, the Pixel 6, is the first phone to have a separate chip dedicated to AI that sits alongside its standard processor. And the chip that runs the iPhone has for the last couple of years contained what Apple calls a “neural engine,” also dedicated to AI. Both chips are better suited to the types of computations involved in training and running machine-learning models on our devices, such as the AI that powers your camera. Almost without our noticing, AI has become part of our day-to-day lives. And it’s changing how we think about computing.

What does that mean? Well, computers haven’t changed much in 40 or 50 years. They’re smaller and faster, but they’re still boxes with processors that run instructions from humans. AI changes that on at least three fronts: how computers are made, how they’re programmed, and how they’re used. Ultimately, it will change what they are for. 

“The core of computing is changing from number-crunching to decision-­making,” says Pradeep Dubey, director of the parallel computing lab at Intel. Or, as MIT CSAIL director Daniela Rus puts it, AI is freeing computers from their boxes. 

More haste, less speed

The first change concerns how computers—and the chips that control them—are made. Traditional computing gains came as machines got faster at carrying out one calculation after another. For decades the world benefited from chip speed-ups that came with metronomic regularity as chipmakers kept up with Moore’s Law. 

But the deep-learning models that make current AI applications work require a different approach: they need vast numbers of less precise calculations to be carried out all at the same time. That means a new type of chip is required: one that can move data around as quickly as possible, making sure it’s available when and where it’s needed. When deep learning exploded onto the scene a decade or so ago, there were already specialty computer chips available that were pretty good at this: graphics processing units, or GPUs, which were designed to display an entire screenful of pixels dozens of times a second. 

Anything can become a computer. Indeed, most household objects, from toothbrushes to light switches to doorbells, already come in a smart version.   ... ' 

ACM ByteCast Announced

ACM announces its podcast, free and open to the public. I will be following and reporting on.  Access information at the link.  Join ACM, it is essential for computing practitioners.

See below, also transcripts of the podcasts.

ACM ByteCast
ACM ByteCast is a podcast series from ACM’s Practitioners Board in which hosts Rashmi Mohan and Jessica Bell interview researchers, practitioners and innovators who are at the intersection of computing research and practice. In each monthly episode, guests will share their experiences, the lessons they’ve learned, and their own visions for the future of computing.

Listen to the latest episode below, or wherever you get your podcasts.

John Hennessy & David Patterson

In this inaugural episode of ACM ByteCast, Rashmi Mohan is joined by 2017 ACM A.M. Turing Laureates John Hennessy and David Patterson. Their conversation touches on the paths that led these two luminaries to pursue computing careers and the "aha moment" that inspired their breakthrough work on RISC microprocessor architecture. They also discuss how they see the future of computing architecture unfolding in the coming years, the need for new memory technologies and better security, the importance of collaboration in innovation, and the promise of the open source community to develop both   ... " 

Can Computers be Conscious?

Don't  absolutely know they cannot compute consciousness unless I know the definition of consciousness being used.   With a very bare definition a computer 'knows it knows something',  say the setting of a single bit.    And can modify it at will.  By scanning and toggling it.  So in that way it is conscious.  Of course nothing approaching human consciousness, not even near it .... but it is a first step.  ...

Why a computer will never be truly conscious
Author  Subhash Kak  Regents Professor of Electrical and Computer Engineering, Oklahoma State University

Subhash Kak does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

Oklahoma State University provides funding as a member of The Conversation US.

Many advanced artificial intelligence projects say they are working toward building a conscious machine, based on the idea that brain functions merely encode and process multisensory information. The assumption goes, then, that once brain functions are properly understood, it should be possible to program them into a computer. Microsoft recently announced that it would spend US$1 billion on a project to do just that.

So far, though, attempts to build supercomputer brains have not even come close. A multi-billion-dollar European project that began in 2013 is now largely understood to have failed. That effort has shifted to look more like a similar but less ambitious project in the U.S., developing new software tools for researchers to study brain data, rather than simulating a brain.

Some researchers continue to insist that simulating neuroscience with computers is the way to go. Others, like me, view these efforts as doomed to failure because we do not believe consciousness is computable. Our basic argument is that brains integrate and compress multiple components of an experience, including sight and smell – which simply can’t be handled in the way today’s computers sense, process and store data.  ... ".... '

Plants as Sensors and Cyborgs

Have been a long time amateur botanist.    So much like the direction suggested here. Designing alerting and circuits inside plants.   Could a field of corn alert the farmer when it is ready to harvest?  Or was being attacked by weeds?   Trees are being attacked by pests in my backyard, could they alert me to  the need for action?   Bring on the idea, ready to test.

Plants Are Oldest Sensors in the World. Could They Be the Future of Computers? 
Fast Company
By Katharine Schwab

The Massachusetts Institute of Technology's Harpreet Sareen suggests plants as a new building material for computer circuits. Based on previous work using leaves as motion detectors and signal transmitters, Sareen's Phytoactuators project enables computers to send electronic signals back to plants, transforming them into alert systems. "Our vision is to have this layer of digital interaction within the plants themselves so we can not only sense signals through them, but also connect our digital responses with the plant's responses," said Sareen, who envisions such systems as tools for "soft notifications," like the arrival of package deliveries triggering signals, to instruct houseplants to retract their leaves. The more advanced Planta Digitalis project has embedded a conductive wire into a plant stem, linked to a computer and functioning as a sensor, as a first step toward designing circuits inside plants. ... "

The MIT Media Lab work this is referencing:
Cyborg Botany: Augmented plants as sensors, displays, and actuators   By Harpreet Sareen 

Jacquard Devices

Notes on new ways to place wearable, immersive computing. Well beyond the loom. 

Jaquard Device Turns Houseplants into Keyboards in Ideaconnection
Designer Ivan Poupyrev discusses his Jacquard device, which can turn everyday objects into computers.  ...

And Levi considers the Jacquard wearable
As a company of firsts, Levi’s has spent over 150 years innovating fashionable, functional clothing. The Jacquard vision centers around expanding the functionality of the clothes people already wear and love. And that’s exactly what we did with Levi’s in creating the Commuter Trucker Jacket with Jacquard by Google woven in. ... "

Is this anywhere today?