Lifeboat Foundation

Circa 2018

Debugging code is drudgery. But SapFix, a new AI hybrid tool created by Facebook engineers, can significantly reduce the amount of time engineers spend on debugging, while also speeding up the process of rolling out new software. SapFix can automatically generate fixes for specific bugs, and then propose them to engineers for approval and deployment to production.

SapFix has been used to accelerate the process of shipping robust, stable code updates to millions of devices using the Facebook Android app — the first such use of AI-powered testing and debugging tools in production at this scale. We intend to share SapFix with the engineering community, as it is the next step in the evolution of automating debugging, with the potential to boost the production and stability of new code for a wide range of companies and research organizations.

Continue reading “Finding and fixing software bugs automatically with SapFix and Sapienz” »

Chinese researchers have reportedly developed artificial intelligence (AI) that can read the minds of Chinese Communist Party (CCP) officials.

A video report detailed the software’s features and attributed it to the Hefei Comprehensive National Science Center, a relatively new institute focused on health and environment, energy research, information management and artificial intelligence.

The technology essentially tests one’s level of loyalty to the CCP. According to the center, it would “further solidify their [members’] confidence and determination to be grateful to the party, listen to the party and follow the party.”

Graphcore and Korea’s Electronics and Telecommunications Research Institute (ETRI) have entered a multi-year partnership to develop new software approaches for high-efficiency AI compute.

Running from 2022 through 2025 and funded by the Korean government, the partnership will combine the world-leading capabilities of ETRI—Korea’s largest public research institute by R&D expenditure and license income—with Graphcore’s proven leadership in developing and commercialising efficient, high-performance compute systems for machine intelligence.

“We just open-sourced an AI model we built that can translate across 200 different languages — many which aren’t supported by current translation systems,” he said. “We call this project No Language Left Behind, and the AI modeling techniques we used from NLLB are helping us make high quality translations on Facebook and Instagram for languages spoken by billions of people around the world.”

Meta invests heavily in AI research, with hubs of scientists across the globe building realistic avatars for use in virtual worlds and tools to reduce hate speech across its platforms 0, among many other weird and wonderful things. This investment allows the company to ensure it stays at the cutting edge of innovation by working with the top AI researchers, while also maintaining a link with the wider research community by open-sourcing projects such as No Languages Left Behind.

The major challenge in creating a translation model that will work across rarer languages is that the researchers have a much smaller pool of data — in this case examples of sentences — to train the model versus, say, English. In many cases, they had to find people who spoke those languages to help them provide the data, and then check that the translations were correct.

A multi-institutional collaboration, which includes the U.S. Department of Energy’s (DOE) Argonne National Laboratory, has created a material that can be used to create computer chips that can do just that. It achieves this by using so-called “neuromorphic” circuitry and computer architecture to replicate brain functions. Purdue University professor Shriram Ramanathan led the team.

“Human brains can actually change as a result of learning new things,” said Subramanian Sankaranarayanan, a paper co-author with a joint appointment at Argonne and the University of Illinois Chicago. “We have now created a device for machines to reconfigure their circuits in a brain-like way.”

With this capability, artificial intelligence-based computers might do difficult jobs more quickly and accurately while using a lot less energy. One example is analyzing complicated medical images. Autonomous cars and robots in space that might rewire their circuits depending on experience are a more futuristic example.

When it comes to our state of mind and emotions, our faces can be quite telling. Facial expression is an essential aspect of nonverbal communication in humans. Even if we cannot explain how we do it, we can usually see in another person’s face how they are feeling. In many situations, reading facial expressions is particularly important. For example, a teacher might do it to check if their students are engaged or bored, and a nurse may do it to check if a patient’s condition has improved or worsened.

Thanks to advances in technology, computers can do a pretty good job when it comes to recognizing faces. Recognizing facial expressions, however, is a whole different story. Many researchers working in the field of artificial intelligence (AI) have tried to tackle this problem using various modeling and classification techniques, including the popular convolutional neural networks (CNNs). However, facial expression recognition is complex and calls for intricate neural networks, which require a lot of training and are computationally expensive.

In an effort to address these issues, a research team led by Dr. Jia Tian from Jilin Engineering Normal University in China has recently developed a new CNN model for facial expression recognition. As described in an article published in the Journal of Electronic Imaging, the team focused on striking a good balance between the training speed, memory usage, and recognition accuracy of the model.

On Tuesday, July 5, space physics and human studies dominated the science agenda aboard the International Space Station. The Expedition 67 crew also reconfigured a US airlock and put a new 3D printer through its paces.

The lack of gravity in space impacts a wide range of physics revealing new phenomena that researchers are studying to improve life for humans on and off the Earth. One such project uses artificial intelligence to adapt complicated glass manufacturing processes in microgravity with the goal of benefitting numerous Earth-and space-based industries. On Tuesday afternoon, NASA

Established in 1958, the National Aeronautics and Space Administration (NASA) is an independent agency of the United States Federal Government that succeeded the National Advisory Committee for Aeronautics (NACA). It is responsible for the civilian space program, as well as aeronautics and aerospace research. Its vision is “To discover and expand knowledge for the benefit of humanity.” Its core values are “safety, integrity, teamwork, excellence, and inclusion.”

The advanced global tech market is set to experience the effect of quantum computing on artificial intelligence in 2022 and beyond. It is essential to integrate quantum computing into artificial intelligence models to boost decision-making abilities more efficiently.

Selmer Bringsjord, and his colleagues have proposed the Lovelace test as a substitute for the flawed Turing test. The test is named after Ada Lovelace.

Bringsjord defined software creativity as passing the Lovelace test if the program does something that cannot be explained by the programmer or an expert in computer code.² Computer programs can generate unexpected and surprising results.³ Results from computer programs are often unanticipated. But the question is, does the computer create a result that the programmer, looking back, cannot explain?

When it comes to assessing creativity (and therefore consciousness and humanness), the Lovelace test is a much better test than the Turing test. If AI truly produces something surprising which cannot be explained by the programmers, then the Lovelace test will have been passed and we might in fact be looking at creativity. So far, however, no AI has passed the Lovelace test.⁴ There have been many cases where a machine looked as if it were creative, but on closer inspection, the appearance of creative content fades.