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The skies were clear as the VISTA X-62A — a one-of-a-kind training aircraft built by Lockheed Martin on an F-16 platform — soared over the Mojave Desert. The cockpit of the high-tech jet is littered with expensive and highly sensitive avionics that enable pilots to perform their missions. But the designers could have crammed in even more technology if it were not for the two pilot seats. Their wish might come true in the not-so-distant future. That’s because this was not your regular sortie.

The training jet was recently reported to have flown 17 hours entirely operated by an artificial intelligence (AI) system, which could open the floodgates for completely autonomous jet fighters and drones. This is the first time that an AI has flown a tactical aircraft for this long.

The VISTA X-62 is perhaps the most powerful and versatile training jet in the world. It’s essentially an upgraded F-16D with Block 40 avionics installed, but with a lot of room for installing and trying out different hardware quickly and easily, that mimics the flight controls of other aircraft, enabling the aircraft itself to act as its own ground simulator. Those who’ve flown the X-62 describe it as a Swiss army knife that they can use to attach lots of different things to the airplane.

https://youtube.com/watch?v=PB6TTzoYLQY&feature=share

Future computers You WON’T See Coming…(analog computing)

An emerging technology called analogue AI accelerators has the potential to completely change the AI sector. These accelerators execute computations using analogue circuits, which are distinct from digital circuits. They have advantages in handling specific kinds of AI algorithms, speed, and energy efficiency. We will examine the potential of this technology, its present constraints, and the use of analogue computing in AI in the future. Join us as we explore the realm of analogue AI accelerators and see how they’re influencing computing’s future. Don’t miss this engaging and educational film; click the subscribe button and check back for additional information about the newest developments in AI technology.

#ai #computing #technology.

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Architects urgently need to get to grips with the existential threat posed by AI or risk, in ChatGPT’s words, “sleepwalking into oblivion”, writes Neil Leach.

In the near future, architects may become a thing of the past. Artificial intelligence (AI) is quickly advancing to a point where it can generate the design of a building completely autonomously. With the potential to create designs faster and with more accuracy than ever before, AI has the potential to revolutionize the architecture industry, leaving traditional architects out of the equation. This could spell the end of the profession as we know it, raising questions of what the future holds for architects in a world of AI-generated buildings.

I did not write the paragraph above. It was generated by ChatGPT, a highly impressive AI text generator that recently launched. Make no mistake: despite its innocuous-sounding name, ChatGPT is no simple chat bot. It is based on GPT3, a massive Generative Pre-Trained Transformer (GPT) that uses Deep Learning to produce human-like text from user-inputted prompts.

ChatGPT has passed the gold-standard exam required to practice medicine in the US — amid rising concerns AI could put white-collar workers out of jobs.

The artificial intelligence program scored between 52.4 and 75 percent across the three-part Medical Licensing Exam (USMLE). Each year’s passing threshold is around 60 percent.

Researchers from tech company AnsibleHealth who did the study said: ‘Reaching the passing score for this notoriously difficult expert exam, and doing so without any human reinforcement, marks a notable milestone in clinical AI maturation.’

Dr. Craig Kaplan discusses Artificial Intelligence — the past, present, and future. He explains how the history of AI, in particular the evolution of machine learning, holds the key to understanding the future of AI. Dr. Kaplan believes we are on an inexorable path towards Artificial General Intelligence (AGI) which is both an existential threat to humanity AND an unprecedented opportunity to solve climate change, povery, disease and other challenges. He explains the likely paths that will lead to AGI and what all of us can do NOW to increase the chances of a positive future.

Chapters.
0:00 Intro.
0:22 Overiew & summary.
0:45 Antecedents of AI
1:15 1956: Birth of the field / Dartmouth conference.
1:33 1956: The Logic Theorist.
1:58 1986: Backprogation algorithm.
2:26 2016: SuperIntelligent AI / Alpha Go.
2:51 Lessons from the past.
3:59 Today’s “Idiot Savant” AI
4:45 Narrow vs. General AI (AGI)
5:15 Deep Mind’s Alpha Zero.
6:19 Demis Hassabis on Alpha Fold.
6:47 Alpha Fold’s amazing performance.
8:03 OpenAI’s ChatGPT
9:16 OpenAI’s DALL-E2
9:50 The future of AI
10:00 AGI is not a tool.
10:30 AGI: Intelligent entity.
10:48 Humans will not be in control.
11:16 The alignment problem.
11:45 Alignment problem is unsolved!
12:45 Likely paths to AGI
13:00 Augmented Reality path to AGI
13:26 Metaverse / Omniverse path to AGI
14:20 AGI: Threat AND Opportunity.
15:10 Get educated — books.
15:48 Get educated — videos.
16:20 Raise awareness.
16:44 How to influence values of AGI
17:52 No guarantees, we must do what we can.
18:47 AGI will learn our values.
19:30 Wrap up / contact info.

LINKS & REFERENCES
Contact:
@iqcompanies.
[email protected].

Websites.
iQStudios website (Free educational videos):
https://www.iqstudios.net/

IQ Company website (Consulting firm specializing in AI & AGI):
https://www.iqco.com/

OpenAI website (Creators of ChatGPT and DALL – E2):

Researchers in the US developed a new energy-based benchmark for quantum advantage and used it to demonstrate noisy intermediate-scale quantum (NISQ) computers that use several orders of magnitude less energy than the world’s most powerful supercomputer. Quantum computing is a branch of computer science that focuses on the development of technologies based on quantum theory principles.

Quantum computing solves problems that are too complex for classical computing by utilizing the unique properties of quantum physics. The question of whether a quantum computer can perform calculations beyond the reach of even the most powerful conventional supercomputer is becoming increasingly relevant as quantum computers become larger and more reliable. This ability, dubbed “quantum supremacy,” marks the transition of quantum computers from scientific curiosity to useful devices. Scientists predict that Quantum computing is better than supercomputers as it performs tasks a million times faster. Quantum computers can handle complex calculations easily because they are built based on quantum principles that go beyond classical physics.

Quantum computers and supercomputers are extremely powerful machines used for complex calculations, problem solving, and data analysis. While both have the potential to revolutionize computing technology, they have significant speed and capability differences. In 2019, Google’s quantum computer performed a calculation that would take the world’s most powerful computer 10,000 years to complete. It is the seed for the world’s first fully functional quantum computer, which will be capable of producing better medicines, developing smarter artificial intelligence, and solving cosmic mysteries. Theoretical physicist John Preskill proposed a formulation of quantum supremacy, or the superiority of quantum computers, in 2012. He dubbed it the moment when quantum computers can perform tasks that ordinary computers cannot. To quickly crunch large amounts of data and achieve a single result, supercomputers employ a traditional computing approach with multiple processors.

Summary: Researchers explain how deep neural networks are able to learn complex physics.

Source: Rice University.

One of the oldest tools in computational physics — a 200-year-old mathematical technique known as Fourier analysis — can reveal crucial information about how a form of artificial intelligence called a deep neural network learns to perform tasks involving complex physics like climate and turbulence modeling, according to a new study.