Lifeboat Foundation

More broadly, biologists are wondering how else deep learning — the AI technique used by both approaches — might be applied to the prediction of protein arrangements, which ultimately dictate a protein’s function. These approaches are cheaper and faster than existing lab techniques such as X-ray crystallography, and the knowledge could help researchers to better understand diseases and design drugs. “There’s a lot of excitement about where things might go now,” says John Moult, a biologist at the University of Maryland in College Park and the founder of the biennial competition, called Critical Assessment of protein Structure Prediction (CASP), where teams are challenged to design computer programs that predict protein structures from sequences.

Deep learning makes its mark on protein-structure prediction.

Whether or not creating an AGI is even possible remains up for debate. Meanwhile, others may cringe at the thought of an AI with the intellect to match and exceed humanity. However, OpenAI has been bullish on the prospect. The company points to the breakthroughs researchers have made in last decade in getting AI algorithms to recognize images, translate languages, and control robots. One of OpenAI’s own AI projects can write fiction like a human can (sort of).

However, creating new AI-based technologies costs a lot of money. Not only does it require programming, but also renting access to thousands of servers. So OpenAI has been seeking funding. “The most obvious way to cover costs is to build a product, but that would mean changing our focus. Instead, we intend to license some of our pre-AGI technologies, with Microsoft becoming our preferred partner for commercializing them,” Altman wrote in a separate blog post.

• The AI Breakthrough Will Require Researchers Burying Their Hatchets.

For a humble, microscopic worm with only 302 neurons, C. elegans has had a lot of firsts. It was the first multicellular animal to have its whole genome sequenced. It was also the spark that lit the connectome fire—the revolutionary idea that mapping the entirety of connections among neurons will unveil secrets of our minds, memory, and consciousness. And if the connectomists are to be believed, a map of individual brains may be the blueprint that will one day hurtle AI into human-level intelligence, or reconstruct an entire human mind in digital form.

More than 30 years ago, a pioneering group of scientists painstakingly traced and reconstructed the roundworm’s neural wiring by hand. The “heroic” effort, unaided by modern computers and brain-mapping algorithms, resulted in the first connectome in 1986.

Yet the “mind of the worm” map had significant lapses. For one, it only focused on one sex, the hermaphrodite—a “female” equivalent that can self-fertilize. This makes it hard to tell which connections are universal for the species, and which are dependent on sex and reproduction. For another, because the effort relied entirely on human beings who get tired, bored, and mess up, the map wasn’t entirely accurate. Even with multiple rounds of subsequent refinements, errors could linger, which would royally screw up any interpretation of results using these maps.

Our current approach to treating chronic pain is drug-based, but a vaccine-based approach can cut addiction out of the equation. In this video, Big Think contributor Lou Reese, co-founder of United Neuroscience, explains how soon we may soon be able to vaccinate people, en masse, against pain!

Squirrels mislabeled as sea lions and dragonflies confused with manhole covers are challenging algorithms to be more resilient to attacks.

A team at Flinders University in South Australia has developed a new vaccine believed to be the first human drug in the world to be completely designed by artificial intelligence (AI).

While drugs have been designed using computers before, this vaccine went one step further being independently created by an AI program called SAM (Search Algorithm for Ligands).

Flinders University Professor Nikolai Petrovsky who led the development told Business Insider Australia its name is derived from what it was tasked to do: search the universe for all conceivable compounds to find a good human drug (also called a ligand).

Auditory stimulus reconstruction is a technique that finds the best approximation of the acoustic stimulus from the population of evoked neural activity. Reconstructing speech from the human auditory cortex creates the possibility of a speech neuroprosthetic to establish a direct communication with the brain and has been shown to be possible in both overt and covert conditions. However, the low quality of the reconstructed speech has severely limited the utility of this method for brain-computer interface (BCI) applications. To advance the state-of-the-art in speech neuroprosthesis, we combined the recent advances in deep learning with the latest innovations in speech synthesis technologies to reconstruct closed-set intelligible speech from the human auditory cortex. We investigated the dependence of reconstruction accuracy on linear and nonlinear (deep neural network) regression methods and the acoustic representation that is used as the target of reconstruction, including auditory spectrogram and speech synthesis parameters. In addition, we compared the reconstruction accuracy from low and high neural frequency ranges. Our results show that a deep neural network model that directly estimates the parameters of a speech synthesizer from all neural frequencies achieves the highest subjective and objective scores on a digit recognition task, improving the intelligibility by 65% over the baseline method which used linear regression to reconstruct the auditory spectrogram. These results demonstrate the efficacy of deep learning and speech synthesis algorithms for designing the next generation of speech BCI systems, which not only can restore communications for paralyzed patients but also have the potential to transform human-computer interaction technologies.

Since its invention by a Hungarian architect in 1974, the Rubik’s Cube has furrowed the brows of many who have tried to solve it, but the 3D logic puzzle is no match for an artificial intelligence system created by researchers at the University of California, Irvine.

DeepCubeA, a deep reinforcement learning algorithm programmed by UCI computer scientists and mathematicians, can find the solution in a fraction of a second, without any specific domain knowledge or in-game coaching from humans. This is no simple task considering that the cube has completion paths numbering in the billions but only one goal state—each of six sides displaying a solid color—which apparently can’t be found through random moves.

For a study published today in Nature Machine Intelligence, the researchers demonstrated that DeepCubeA solved 100 percent of all test configurations, finding the shortest path to the goal state about 60 percent of the time. The algorithm also works on other combinatorial games such as the sliding tile puzzle, Lights Out and Sokoban.

https://www.youtube.com/watch?v=ytva8DDV_Ic

What Is Big Data? & How Big Data Is Changing The World! https://www.facebook.com/singularityprosperity/videos/439181406563439/

In this video, we’ll be discussing big data – more specifically, what big data is, the exponential rate of growth of data, how we can utilize the vast quantities of data being generated as well as the implications of linked data on big data.

Continue reading “Artificial Intelligence: A History” »