Lifeboat Foundation: Safeguarding Humanity
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As neural networks become more powerful, algorithms have become capable of turning ordinary text into images, animations and even short videos. These algorithms have generated significant controversy. An AI-generated image recently won first prize in an annual art competition while the Getty Images stock photo library is currently taking legal action against the developers of an AI art algorithm that it believes was unlawfully trained using Getty’s images.

So the music equivalent of these systems shouldn’t come as much surprise. And yet the implications are extraordinary.

A group of researchers at Google have unveiled an AI system capable of turning ordinary text descriptions into rich, varied and relevant music. The company has showcased these capabilities using descriptions of famous artworks to generate music.

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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.

These hurdles include problems that are impossible for computers to solve and problems that are theoretically solvable but in practice are beyond the capabilities of even the most powerful versions of today’s computers imaginable. Mathematicians and computer scientists attempt to determine whether a problem is solvable by trying them out on an imaginary machine.

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Evolution of multicellularity from early unicellular ancestors is arguably one of the most important transitions since the origin of life1,2. Multicellularity is often associated with higher nutrient uptake3, better defense against predation, cell specialization and better division of labor4. While many single-celled organisms exhibit both solitary and colonial existence3,5,6, the organizing principles governing the transition and the benefits endowed are less clear. Using the suspension-feeding unicellular protist Stentor coeruleus, we show that hydrodynamic coupling between proximal neighbors results in faster feeding flows that depend on the separation between individuals. Moreover, we find that the accrued benefits in feeding current enhancement are typically asymmetric– individuals with slower solitary currents gain more from partnering than those with faster currents. We find that colony-formation is ephemeral in Stentor and individuals in colonies are highly dynamic unlike other colony-forming organisms like Volvox carteri 3. Our results demonstrate benefits endowed by the colonial organization in a simple unicellular organism and can potentially provide fundamental insights into the selective forces favoring early evolution of multicellular organization.

Suspension-feeding unicellular protists inhabit a fluid world dominated by viscous forces that limit prey transport for feeding 3,7,8. Using either flagella or cilia, many of these organisms generate microcurrents that actively transport dissolved nutrients and smaller prey critical for their nutrition 3,9,10. A protist’s ability to favorably alter its feeding current so as to enhance feeding rate would therefore be beneficial to its survival. Can colony formation enable unicellular protists to enhance their feeding flows? Colonial protists have been suggested to generate stronger flows by combining individual feeding microcurrents of neighboring colony members. Colony forming protists can broadly be classified into two categories depending on presence (or absence) of physical linkages between colony members.

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German automaker Mercedes-Benz claims to have achieved Level 3 autonomy — “conditionally automated” vehicles that can monitor their driving environment and make informed decisions on behalf of the driver, but still require humans to occasionally take over — in the United States, an incremental but noteworthy step towards a future void of steering wheels and foot pedals.

“It is a very proud moment for everyone to continue this leadership and celebrate this monumental achievement as the first automotive company to be certified for Level 3 conditionally automated driving in the US market,” said Mercedes-Benz USA CEO Dimitris Psillakis in a statement.

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The automobile business is continuously evolving, and as technology advances, we are seeing a shift toward a future of automated robots in the car maintenance sector. This move is expected to have a number of advantages, including greater accuracy and efficiency in automotive maintenance, as well as the capacity to work on older vehicles that traditional mechanics may be unfamiliar with.

As the owner of an older family truck, I have direct knowledge of the difficulty in finding a mechanic willing to work on their vehicle even if it is well kept up. With the emergence of automated robots, there will be no need to rely on human mechanics, as robots will be capable of doing the required jobs with ease. They will be able to detect faults and undertake routine maintenance, such as replacing belts, radiators, and other parts, without specialist training or knowledge.

The benefits of automated robots in automotive repair go beyond increased productivity. The robots will be built to work on a wide range of automobiles, regardless of age, allowing owners of older vehicles to maintain their vehicles without having to worry about finding a mechanic ready to work on them. Furthermore, automated robots will improve automotive maintenance accuracy since they will be equipped with cutting-edge technology and will be able to complete duties with precision and speed.

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Of those 100 proteins, the team created five of the artificial proteins and tested their functionality in cells, seeing how well they compared to an enzyme found in chicken eggs aptly named “hen egg white lysozyme” (HEWL). Two of the proteins demonstrated activity similar to HEWL, breaking down bacteria’s cell walls.

“The enzymes work (out-of-the-box) as well as proteins that have evolved over millions of years of evolution,” Madani said. The team also found that the model was able to capture evolutionary patterns, without specifically being trained to do so.

While AI has been used to generate proteins, this study differs a bit from prior research and further expands the idea of what is possible with language models.

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