Lifeboat Foundation

Here is a list of some of the most popular quantum algorithms highlighting the significant impact quantum can have on the classical world:

Shor’s Algorithm

Our entire data security systems are based on the assumption that factoring integers with a thousand or more digits is practically impossible. That was until Peter Shor in 1995 proposed that quantum mechanics allows factorisation to be performed in polynomial time, rather than exponential time achieved using classical algorithms.

Quantum computing has entered a bit of an awkward period. There have been clear demonstrations that we can successfully run quantum algorithms, but the qubit counts and error rates of existing hardware mean that we can’t solve any commercially useful problems at the moment. So, while many companies are interested in quantum computing and have developed software for existing hardware (and have paid for access to that hardware), the efforts have been focused on preparation. They want the expertise and capability needed to develop useful software once the computers are ready to run it.

For the moment, that leaves them waiting for hardware companies to produce sufficiently robust machines—machines that don’t currently have a clear delivery date. It could be years; it could be decades. Beyond learning how to develop quantum computing software, there’s nothing obvious to do with the hardware in the meantime.

But a company called QuEra may have found a way to do something that’s not as obvious. The technology it is developing could ultimately provide a route to quantum computing. But until then, it’s possible to solve a class of mathematical problems on the same hardware, and any improvements to that hardware will benefit both types of computation. And in a new paper, the company’s researchers have expanded the types of computations that can be run on their machine.

Google has trained an artificial intelligence, named SingSong, that can generate a musical backing track to accompany people’s recorded singing.

To develop it, Jesse Engel and his colleagues at Google Research used an algorithm to separate the instrumental and vocal parts from 46,000 hours of music and then fine-tuned an existing AI model – also created by Google Research, but for generating speech and piano music – on those pairs of recordings.

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.

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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.

Experts are divided on whether practical applications are just around the corner.

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.

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https://youtube.com/watch?v=WLehkWESDJ8

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.

Continue reading “AI is putting our jobs as architects unquestionably at risk” »

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.

Continue reading “Artificial Intelligence: Past, Present, and Future” »