Awesome, not awesome.
“…. Maybe [deep learning] could help to refine approximate structure predictions; report on how confident the algorithm is in a folding prediction; or model interactions between proteins. And although computational predictions aren’t yet accurate enough to be widely used in drug design, the increasing accuracy allows for other applications, such as understanding how a mutated protein contributes to disease or knowing which part of a protein to turn into a vaccine for immunotherapy.” — Matthew Houston, Reporter Learn More from Nature >
“While [artificial intelligence to moderate content and automate the removal of harmful posts] may appear to represent a turning point in the debate around hate speech on the internet, recent research has shown that [it is] still far from being able to distinguish context or intent. If such AI tools are entrusted with the power to police content online, they have the potential to suppress legitimate speech and censor the use of specific words, particularly by vulnerable groups.” — Dennys Antonialli, Law professor Learn More from WIRED >
What we’re reading.
1/ Fear of being of China making faster progress than the US in the field of artificial intelligence may push us to create technology that makes everyone worse off. Learn More from The Intercept >
2/ As we offload more of our thinking to AI processes, we may be nearing a “world of knowledge without understanding” in which machines tell us what to do — not the other way around. Learn More from The New Yorker >
3/ Elon Musk is betting Tesla’s future on self-driving technology, telling Wall Street they will have millions of automated vehicles on the road within the next year and that it will grow the company’s profit margin from 19% to 30%. Learn More from Axios >
4/ Microsoft invests $1 billion in OpenAI, the company that’s on a mission to build artificial general intelligence that benefits all of humanity. Learn More from OpenAI >
5/ AI researchers develop algorithms that can detect when other AI systems are responsible for writing text that is convincing enough to confuse humans. Learn More from MIT Technology Review >
6/ Forensic Architecture uses machine learning to analyze satellite imagery and detect if governments fired tear gas canisters at citizens. Learn More from WIRED >
7/ The US military starts using autonomous surveillance technology to identify when trespassers are nearing bases. Learn More from MIT Technology Review >
Links from the community.
“Understanding ‘Winograd Fast Convolution’” by Deepak Mangla. Learn More from Noteworthy >
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China and the US racing for AI supremacy might drag us all to the bottom was originally published in Machine Learnings on Medium, where people are continuing the conversation by highlighting and responding to this story.