[1] DeepMind — the AI lab backed by Google's parent company Alphabet — claims to have improved upon Codex in key areas with AlphaCode, a system that can write "competition-level" code. In programming competitions hosted on Codeforces, a platform for programming contests, DeepMind claims that AlphaCode achieved an average ranking within the top 54.3% across 10 recent contests with more than 5,000 participants each.
DeepMind principal research scientist Oriol Vinyals says it's the first time that a computer system has
achieved such a competitive level in all programming competitions. "AlphaCode [can] read the natural language descriptions of an algorithmic problem and produce code that not only compiles, but is correct," he added in a statement. "[It] indicates that there is still work to do to achieve the level of the highest performers, and advance the problem-solving capabilities of our AI systems. We hope this benchmark will lead to further innovations in problem-solving and code generation."
>> Read more. [2] GitHub has launched a new feature that allows companies and developers to offer project sponsors special access to a private repository.
The Microsoft-owned code-hosting platform first introduced GitHub Sponsors back in 2019, enabling anyone to donate to open source projects and maintainers
who dedicate their time to supporting critical software. GitHub later extended the Sponsors initiative to support developer teams and organizations.
[3] AI, like humans, learns from examples. Given enough data and time, an
AI model can make sense of the statistical relationships well enough to generate predictions. That's how OpenAI's GPT-3 writes text from poetry to computer code, and how apps like Google Lens recognize objects such as lampshades in photos of bedrooms.
Historically, the data to train as well as test AI has come mostly from public sources on the web. But these sources are flawed. For example, Microsoft quietly
removed a dataset with more than 10 million images of people after it came to light that some subjects weren't aware that they'd been included. Datasets created from local TV news segments are likely to negatively portray Black men because the news often covers crime in a
sensationalized,
racist way. And the data used to train AI to detect people's expressed emotions from their faces have been found to contain more happy faces than sad ones because users tend to post happier images of themselves on social media.
As the AI community grapples with the issues around — and the consequences of — using public data, researchers have begun exploring potentially less problematic ways of creating AI datasets. Some proposals
gamify the collection process, while others monetize it. But while there isn't consensus on approach, there's a growing recognition of the harm perpetuated by data collection in the past — and the need to address it.
>> Read more. 
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