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1950s Fighter Jet Air Computer Shows What Analog Could Do

Imagine you’re a young engineer whose boss drops by one morning with a sheaf of complicated fluid dynamics equations. “We need you to design a system to solve these equations for the latest fighter jet,” bossman intones, and although you groan as you recall the hell of your fluid dynamics courses, you realize that it should be easy enough to whip up a program to do the job. But then you remember that it’s like 1950, and that digital computers — at least ones that can fit in an airplane — haven’t been invented yet, and that you’re going to have to do this the hard way.

The scenario is obviously contrived, but this peek inside the Bendix MG-1 Central Air Data Computer reveals the engineer’s nightmare fuel that was needed to accomplish some pretty complex computations in a severely resource-constrained environment. As [Ken Shirriff] explains, this particular device was used aboard USAF fighter aircraft in the mid-50s, when the complexities of supersonic flight were beginning to outpace the instrumentation needed to safely fly in that regime. Thanks to the way air behaves near the speed of sound, a simple pitot tube system for measuring airspeed was no longer enough; analog computers like the MG-1 were designed to deal with these changes and integrate them into a host of other measurements critical to the pilot.

To be fair, [Ken] doesn’t do a teardown here, at least in the traditional sense. We completely understand that — this machine is literally stuffed full of a mind-boggling number of gears, cams, levers, differentials, shafts, and pneumatics. Taking it apart with the intention of getting it back together again would be a nightmare. But we do get some really beautiful shots of the innards, which reveal a lot about how it worked. Of particular interest are the torque-amplifying servo mechanism used in the pressure transducers, and the warped-plate cams used to finely adjust some of the functions the machine computes.

How One of the Most Important Algorithms in Math Made Color TV Possible

A key algorithm that quietly empowers and simplifies our electronics is the Fourier transform, which turns the graph of a signal varying in time into a graph that describes it in terms of its frequencies.

Packaging signals that represent sounds or images in terms of their frequencies allows us to analyze and adjust sound and image files, Richard Stern, professor of electrical and computer engineering at Carnegie Mellon University, tells Popular Mechanics. This mathematical operation also makes it possible for us to store data efficiently.

The invention of color TV is a great example of this, Stern explains. In the 1950s, television was just black and white. Engineers at RCA developed color television, and used Fourier transforms to simplify the data transmission so that the industry could introduce color without tripling the demands on the channels by adding data for red, green, and blue light. Viewers with black-and-white TVs could continue to see the same images as they saw before, while viewers with color TVs could now see the images in color.

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

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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AI is putting our jobs as architects unquestionably at risk

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.

Artificial Intelligence: Past, Present, and Future

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):

8 Candidate Alien Signals From 5 Stars Found by AI Algorithm with Dr. Cherry Ng and Peter Ma

Head to https://squarespace.com/eventhorizon to save 10% off your first purchase of a website or domain using code eventhorizon.
Did We Find Them? 8 Candidate Alien Signals Found with a new AI Algorithm by SETI.

A deep-learning search for technosignatures of 820 nearby stars.
https://seti.berkeley.edu/ml_gbt/MLSETI_NatAstron_arxiv3.pdf.

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ChatGPT is about to get even better and Microsoft’s Bing could win big

Google worked to reassure investors and analysts on Thursday during its quarterly earnings call that it’s still a leader in developing AI. The company’s Q4 2022 results were highly anticipated as investors and the tech industry awaited Google’s response to the popularity of OpenAI’s ChatGPT, which has the potential to threaten its core business.

During the call, Google CEO Sundar Pichai talked about the company’s plans to make AI-based large language models (LLMs) like LaMDA available in the coming weeks and months. Pichai said users will soon be able to use large language models as a companion to search. An LLM, like ChatGPT, is a deep learning algorithm that can recognize, summarize and generate text and other content based on knowledge from enormous amounts of text data. Pichai said the models that users will soon be able to use are particularly good for composing, constructing and summarizing.

“Now that we can integrate more direct LLM-type experiences in Search, I think it will help us expand and serve new types of use cases, generative use cases,” Pichai said. “And so, I think I see this as a chance to rethink and reimagine and drive Search to solve more use cases for our users as well. It’s early days, but you will see us be bold, put things out, get feedback and iterate and make things better.”

Pichai’s comments about the possible ChatGPT rival come as a report revealed this week that Microsoft is working to incorporate a faster version of ChatGPT, known as GPT-4, into Bing, in a move that would make its search engine, which today has only a sliver of search market share, more competitive with Google. The popularity of ChatGPT has seen Google reportedly turning to co-founders Larry Page and Sergey Brin for help in combating the potential threat. The New York Times recently reported that Page and Brin had several meetings with executives to strategize about the company’s AI plans.

During the call, Pichai warned investors and analysts that the technology will need to scale slowly and that he sees large language usage as still being in its “early days.” He also said that the company is developing AI with a deep sense of responsibility and that it’s going to be careful when launching AI-based products, as the company plans to initially launch beta features and then slowly scale up from there.

He went on to note that Google will provide new tools and APIs for developers, creators and partners to empower them to build their own applications and discover new possibilities with AI.