It sometimes presents incorrect steps to arrive at the answer, because it is designed to base conclusions on precedent. And a precedent based on a given data set is limited to the confines of the data set. This, says Microsoft, leads to “increased costs, memory, and computational overheads.”
AoT to the rescue. The algorithm evaluates whether the initial steps—” thoughts,” to use a word generally associated only with humans—are sound, thereby avoiding a situation where an early wrong “thought” snowballs into an absurd outcome.
Though not expressly stated by Microsoft, one can imagine that if AoT is what it’s cracked up to be, it might help mitigate the so-called AI “hallucinations”—the funny, alarming phenomenon whereby programs like ChatGPT spits out false information. In one of the more notorious examples, in May 2023, a lawyer named Stephen A. Schwartz admitted to “consulting” ChatGPT as a source when conducting research for a 10-page brief. The problem: The brief referred to several court decisions as legal precedents… that never existed.
At a crucial time when the development and deployment of AI are rapidly evolving, experts are looking at ways we can use quantum computing to protect AI from its vulnerabilities.
Machine learning is a field of artificial intelligence where computer models become experts in various tasks by consuming large amounts of data, instead of a human explicitly programming their level of expertise. These algorithms do not need to be taught but rather learn from seeing examples, similar to how a child learns.
This book, ‘The Singularity Is Near’, predicts the future. However, unlike most best-selling futurology books, its author, Kurzweil, is a renowned technology expert. His insights into the future are not technocratic wild fantasies but are rooted in his profound contemplation of technological principles.
This audio informs us that, due to Moore’s Law, the pace of human technological advancement in the future will far exceed our expectations. By 2045, we will reach the technological ‘Singularity’, which will profoundly alter our human condition, and technology may even enable humans to conquer the universe within a millennium.
The author, Ray Kurzweil, is a true tech maestro. He has been inducted into the National Inventors Hall of Fame in the U.S., is a recipient of the National Medal of Technology, holds 13 honorary doctorates, has been lauded by three U.S. presidents, and is dubbed by the media as the ‘rightful heir to Thomas Edison’.
In the audio, you will hear:
Moore’s Law has been around for 40 years; can it continue? Why is it said that by 2045, humans will reach a technological Singularity? Why are future humans described as a set of algorithms? Is artistic creation the last bastion between humans and artificial intelligence?
🔒 Keep Your Digital Life Private and Stay Safe Online: https://nordvpn.com/safetyfirst. Welcome to our channel! In this exciting video, we delve into the fascinating realm of artificial intelligence (AI) and explore the question that has intrigued tech enthusiasts and experts alike: “How powerful will AI be in 2030?” Join us as we embark on a captivating journey into the future of AI, examining the possibilities, advancements, and potential impact that await us.
In the next decade, AI is poised to revolutionize numerous industries and transform the way we live and work. As we peer into the crystal ball of technological progress, we aim to shed light on the potential power and capabilities that AI could possess by 2030. Brace yourself for mind-blowing insights and expert analysis that will leave you in awe.
We begin by exploring the current state of AI and its rapid advancements. From machine learning algorithms to neural networks and deep learning models, AI has already demonstrated exceptional prowess in various fields, including healthcare, finance, transportation, and more. By building upon these achievements, AI is set to evolve exponentially, opening doors to a future where intelligent machines collaborate seamlessly with humans.
Throughout this video, we delve into key areas where AI is expected to make significant strides by 2030. We discuss advancements in natural language processing, computer vision, robotics, and autonomous systems. Witness the potential of AI-powered virtual assistants, autonomous vehicles, medical diagnostics, and even the integration of AI in our daily lives.
To provide a comprehensive perspective, we draw insights from leading AI researchers, industry pioneers, and thought leaders who offer their expert opinions on the future trajectory of AI. Their invaluable insights help us paint a vivid picture of the exciting possibilities that await us in the next decade.
Join us on this thought-provoking journey into the future, as we ponder the ethical implications, challenges, and potential risks that arise with the growing power of AI. By understanding the trajectory of AI development, we can prepare ourselves for a future where humans and intelligent machines coexist harmoniously.
A glimpse into the dynamics between a man and a self-conscious machine.
Artificial Intelligence (AI) in a nutshell
Artificial intelligence (AI) and Cognitive robotics are the two stalwart fields of design and engineering, seizing all the spotlight lately. Artificial intelligence is a human intelligence simulation processed by machines, whereas cognitive robotics is the corollary of robotics and cognitive science that deals with cognitive phenomena like learning, reasoning, perception, anticipation, memory, and attention. Robotics is a part of AI, where the robots are programmed with artificial intelligence to perform the tasks, and AI is the program or algorithm that a robot employs to perform cognitive functions. In simpler terms, a robot is a machine, and AI is the intellect fuel fortified to ignite perceptual abilities in a machine.
For decades, electronics engineers have been trying to develop increasingly advanced devices that can perform complex computations faster and consuming less energy. This has become even more salient after the advent of artificial intelligence (AI) and deep learning algorithms, which typically have substantial requirements both in terms of data storage and computational load.
A promising approach for running these algorithms is known as analog in-memory computing (AIMC). As suggested by its name, this approach consists of developing electronics that can perform computations and store data on a single chip. To realistically achieve both improvements in speed and energy consumption, this approach should ideally also support on-chip digital operations and communications.
Researchers at IBM Research Europe recently developed a new 64-core mixed-signal in-memory computing chip based on phase-change memory devices that could better support the computations of deep neural networks. Their 64-core chip, presented in a paper in Nature Electronics, has so far attained highly promising results, retaining the accuracy of deep learning algorithms, while reducing computation times and energy consumption.
Many users who want more from their smartphones are glad to use a plethora of advanced features, mainly for health and entertainment. Turns out that these features create a security risk when making or receiving calls.
Researchers from Texas A&M University and four other institutions created malware called EarSpy, which uses machine learning algorithms to filter caller information from ear speaker vibration data recorded by an Android smartphone’s own motion sensors, without overcoming any safeguards or needing user permissions.
In their 1982 paper, Fredkin and Toffoli had begun developing their work on reversible computation in a rather different direction. It started with a seemingly frivolous analogy: a billiard table. They showed how mathematical computations could be represented by fully reversible billiard-ball interactions, assuming a frictionless table and balls interacting without friction.
This physical manifestation of the reversible concept grew from Toffoli’s idea that computational concepts could be a better way to encapsulate physics than the differential equations conventionally used to describe motion and change. Fredkin took things even further, concluding that the whole Universe could actually be seen as a kind of computer. In his view, it was a ‘cellular automaton’: a collection of computational bits, or cells, that can flip states according to a defined set of rules determined by the states of the cells around them. Over time, these simple rules can give rise to all the complexities of the cosmos — even life.
He wasn’t the first to play with such ideas. Konrad Zuse — a German civil engineer who, before the Second World War, had developed one of the first programmable computers — suggested in his 1969 book Calculating Space that the Universe could be viewed as a classical digital cellular automaton. Fredkin and his associates developed the concept with intense focus, spending years searching for examples of how simple computational rules could generate all the phenomena associated with subatomic particles and forces3.
In a recent study published in the journal Frontiers in Medicine, researchers evaluated fluorescence optical imaging (FOI) as a method to accurately and rapidly diagnose rheumatic diseases of the hands.
They used machine learning algorithms to identify the minimum number of FOI features to differentiate between osteoarthritis (OA), rheumatoid arthritis (RA), and connective tissue disease (CTD). Of the 20 features identified as associated with the conditions, results indicate that reduced sets of features between five and 15 in number were sufficient to diagnose each of the diseases under study accurately.
עברית (Hebrew)
