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In 1979 the Macintosh personal computer existed only as the pet idea of Jef Raskin, a veteran of the Apple II team, who had proposed that Apple Computer Inc. make a low-cost “appliance”-type computer that would be as easy to use as a toaster. Mr. Raskin believed the computer he envisioned, which he called Macintosh, could sell for US $1000 if it was manufactured in high volume and used a powerful microprocessor executing tightly written software.

Mr. Raskin’s proposal did not impress anyone at Apple Computer enough to bring much money from the board of directors or much respect from Apple engineers. The company had more pressing concerns at the time: the major Lisa workstation project was getting under way, and there were problems with the reliability of the Apple III, the revamped version of the highly successful Apple II.

IEEE Spectrum

An interesting look at the design of Apple Mac ...

Stephen Hawking and I created his final theory of the cosmos – here’s what it reveals about the origins of time and life

The late physicist Stephen Hawking first asked me to work with him to develop “a new quantum theory of the Big Bang” in 1998. What started out as a doctoral project evolved over some 20 years into an intense collaboration that ended only with his passing on March 14 2018.

The enigma at the centre of our research throughout this period was how the Big Bang could have created conditions so perfectly hospitable to life. Our answer is being published in a new book, On the Origin of Time: Stephen Hawking’s Final Theory.

Questions about the ultimate origin of the cosmos, or universe, take physics out of its comfort zone. Yet this was exactly where Hawking liked to venture. The prospect — or hope — to crack the riddle of cosmic design drove much of Hawking’s research in cosmology. “To boldly go where Star Trek fears to tread” was his motto – and also his screen saver.

The Conversation

500 Top Technologists and Elon Musk Demand Immediate Pause of Advanced AI Systems
Steve Wozniak and Stuart Russell were among the signatories of an open letter warning advanced models pose “profound risks to society and humanity."

A wide-ranging coalition of more than 500 technologists, engineers, and AI ethicists have signed an open letter calling on AI labs to immediately pause all training on any AI systems more powerful than Open AI’s recently released GPT-4 for at least six months.

The signatories, which include Apple co-founder Steve Wozniak and “based AI” developer Elon Musk, warn these advanced new AI models could pose “profound risks to society and humanity,” if allowed to advance without sufficient safeguards. If companies refuse to pause development, the letter says governments should whip out the big guns and institute a mandatory moratorium.

“Advanced AI could represent a profound change in the history of life on Earth, and should be planned for and managed with commensurate care and resources,” the letter reads. “Unfortunately, this level of planning and management is not happening, even though recent months have seen AI labs locked in an out-of-control race to develop and deploy ever more powerful digital minds that no one—not even their creators—can understand, predict, or reliably control.”

The letter was released by The Future of Life Institute, an organization self-described as focused on steering technologies away from perceived large-scale risks to humanity. Those primary risk groups include AI, biotechnology, nuclear weapons, and climate change. The group’s concerns over AI systems rest on the assumption that those systems, “are now becoming human-competitive at general tasks.” That level of sophistication, the letter argues, could lead to a near future where bad actors use AI to flood the internet with propaganda, make once stable jobs redundant, and develop “nonhuman minds” that could out-complete or “replace” humans.

Gizmodo

Generative AI is overrated, long live old-school AI !

TLDR; Don't be dazzled by generative AI's creative charm! Predictive AI, though less flashy, remains crucial for solving real-world challenges and unleashing AI's true potential. By merging the powers of both AI types and closing the prototype-to-production gap, we'll accelerate the AI revolution and transform our world. Keep an eye on both these AI stars to witness the future unfold.

Throughout 2022, generative AI captured the public’s imagination. Now that GPT-4 is out, the hype is poised to reach new heights.

With the late 2022 release(s) of Stable Diffusion, Dall-E2, and ChatGPT, people could engage with AI first-hand, watching with awe as seemingly intelligent systems created art, composed songs, penned poetry, and wrote passable college essays.

Only a few months later, some investors have become only interested in companies building generative AI, relegating those working on predictive models to “old school” AI.

However, generative AI alone won’t fulfill the promise of the AI revolution. The sci-fi future that many people anticipate accompanying the widespread adoption of AI depends on the success of predictive models. Self-driving cars, robotic attendants, personalized healthcare, and many other innovations hinge on perfecting “old school” AI.

Encord

How many numbers have found their way into your brain today? 10? 100? 1,000?

What if you include the number of steps and heartbeats from your smartwatch? Likes and followers on social media? Numbers at work, from your bank, in games, apps, and in your inbox? According to estimates, we now collectively generate more numbers every day than all of humankind combined scraped together between creation and year 2010.

Now, try to stop and think for a moment how these numbers, consciously and unconsciously, make their way into your brain and influence the decisions you make every single day. Because these numbers do fool you, those little bastards. Numbers at work tweak your motivation and effort. Social media numbers make the social scene a competitive nightmare and create winners and losers. Your Fitbit numbers make you run faster in the short run, but eventually turn running into a work and a chore. And every single number you let into your brain serves as a frame of reference against which you compare and evaluate the world.

[...] So be aware: Numbers are everywhere, you believe them to be true (even when they are not), and they bias your decisions in more ways than you can imagine. Maybe you need a detox.

TIME Ideas

50 Years Later, We’re Still Living in the Xerox Alto’s World

I’m sitting in front of a computer, looking at its graphical user interface with overlapping windows on a high-resolution screen. I interact with the computer by pointing and clicking with a mouse and typing on a keyboard. I’m using a word processor with the core features and functions of Microsoft Word, Google Docs, or LibreOffice’s Writer, along with an email client that could be mistaken for a simplified version of Apple Mail, Microsoft Outlook, or Mozilla Thunderbird. This computer runs other software, written using object-oriented programming, just like the popular programming languages Python, C++, C#, Java, JavaScript, and R. Its networking capabilities can link me to other computers and to high-quality laser printers.

You are probably thinking, “So what? My computer has all that too.” But the computer in front of me is not today’s MacBook, ThinkPad, or Surface computer. Rather, it’s half-century-old hardware running software of the same vintage, meticulously restored and in operation at the Computer History Museum’s archive center. Despite its age, using it feels so familiar and natural that it’s sometimes difficult to appreciate just how extraordinary, how different it was when it first appeared.

I’m talking about the Xerox Alto, which debuted in the early spring of 1973 at the photocopying giant’s newly established R&D laboratory, the Palo Alto Research Center (PARC). The reason it is so uncannily familiar today is simple: We are now living in a world of computing that the Alto created.

IEEE Spectrum

Limits to computing: A computer scientist explains why even in the age of AI, some problems are just too difficult to solve

Empowered by artificial intelligence technologies, computers today can engage in convincing conversations with people, compose songs, paint paintings, play chess and go, and diagnose diseases, to name just a few examples of their technological prowess.

There are two aspects to a computer’s power: the number of operations its hardware can execute per second and the efficiency of the algorithms it runs. The hardware speed is limited by the laws of physics. Algorithms – basically sets of instructions – are written by humans and translated into a sequence of operations that computer hardware can execute. Even if a computer’s speed could reach the physical limit, computational hurdles remain due to the limits of algorithms.

[...] Practical algorithms that address these problems in the real world can only offer approximations, though the approximations are improving. Whether there are efficient polynomial-time algorithms that can solve NP-complete problems is among the seven millennium open problems posted by the Clay Mathematics Institute at the turn of the 21st century, each carrying a prize of US$1 million.

[...] Can a full-fledged quantum computer be built to factor integers and solve other problems? Some scientists believe it can be. Several groups of scientists around the world are working to build one, and some have already built small-scale quantum computers.

Nevertheless, like all novel technologies invented before, issues with quantum computation are almost certain to arise that would impose new limits.

The Conversation

Our economic future depends on young reformers, not ineffective revolutionaries

Many of our capitalist institutions have been damaged by cronyism, greed and a short-term mindset. But capitalism is more than its faults and the unpleasant outcomes brought on by a selfish class.
Revitalizing capitalism begins with reform, which means introducing changes within the existing structure. However, the newest cohort to enter corporate life, Gen Z, has little confidence in the corporate system. They are unwilling to play a game where they don’t trust the rules or referees.

While we don’t have an extensive amount of research on this cohort, we do know that Gen Z seems to be less involved in civil engagement and reluctant to engage in teamwork.

And according to a recent study from Ethisphere, Gen Z both embraces the strongest ethical commitments and is the least likely to report bad behaviour at work. Nearly 39 per cent of Gen Z respondents chose not to report misconduct when they witnessed it — an 11-point gap from their Gen X and Boomer colleagues.

Gen Z employees don’t believe reporting corporate misbehaviour is worthwhile because they fear retaliation and have no confidence corrective action will be taken. So how can Gen Z be effective agents of reform in a system they don’t believe in?

[...] Under conventional capitalist thinking, it’s worth being trustworthy if it leads to cost savings. We need to depart from this reductionist view.

When the youth don’t trust the system, the highest priority of the establishment must be rebuilding that trust. Seeking to be seen as trustworthy is what will convince the next generation skeptical of our institutions to work with us on reform.

The Conversation

Science has finally cracked the mystery of why so many people believe in conspiracy theories

When it comes to the spread of cockamamie conspiracy theories, Twitter was a maximum viable product long before Elon Musk paid $44 billion for the keys. But as soon as he took the wheel, Musk removed many of the guardrails Twitter had put in place to keep the craziness in check. Anti-vaxxers used an athlete's collapse during a game to revive claims that COVID-19 vaccines kill people. (They don't.) Freelance journalists spun long threads purporting to show that Twitter secretly supported Democrats in 2020. (It didn't.) Musk himself insinuated that the attack on Nancy Pelosi's husband was carried out by a jealous boyfriend. (Nope.) Like a red thread connecting clippings on Twitter's giant whiteboard, conspiratorial ideation spread far and wide.

Social scientists are closing in on some answers. The personality traits known as the "Dark Triad" — that's narcissism, psychopathy, and a tendency to see the world in black-or-white terms — play a part. So do political beliefs, particularly populism and a tolerance for political violence. Cognitive biases, like believing only evidence that confirms what you already think, also make people more vulnerable.

But according to new research, it isn't ignorance that makes people most likely to buy into conspiratorial thinking, or social isolation or mental illness. It's a far more prevalent and pesky personality quirk: overconfidence.

Business Insider India

Preprint DOI

Nearly 300 MSI motherboards will run any code in Secure Boot, no questions asked
'I believe they made this change deliberately' claims researcher

The Secure Boot process on almost 300 different PC motherboard models manufactured by Micro-Star International (MSI) isn't secure, which is particularly problematic when "Secure" is part of the process description.

Dawid Potocki, an open source security researcher and student based in New Zealand, found last month that some MSI motherboards with certain firmware versions allow arbitrary binaries to boot despite Secure Boot policy violations.

Secure Boot is a PC security standard intended to ensure that devices boot only software trusted by the maker of the hardware. The device firmware is supposed to check the cryptographic signature of each piece of boot software, including UEFI firmware drivers, EFI applications, and the operating system. That's the theory, anyway.

The Register