From AIatMeta.
Today we’re introducing SceneScript, a novel method for reconstructing environments and representing the layout of physical spaces from @RealityLabs Research.
Details ➡️ https://bit.ly/3x2cOzh.
SceneScript is able to directly infer a room’s geometry using end-to-end machine learning…
The term “artificial general intelligence” (AGI) has become ubiquitous in current discourse around AI. OpenAI states that its mission is “to ensure that artificial general intelligence benefits all of humanity.” DeepMind’s company vision statement notes that “artificial general intelligence…has the potential to drive one of the greatest transformations in history.” AGI is mentioned prominently in the UK government’s National AI Strategy and in US government AI documents. Microsoft researchers recently claimed evidence of “sparks of AGI” in the large language model GPT-4, and current and former Google executives proclaimed that “AGI is already here.” The question of whether GPT-4 is an “AGI algorithm” is at the center of a lawsuit filed by Elon Musk against OpenAI.
Given the pervasiveness of AGI talk in business, government, and the media, one could not be blamed for assuming that the meaning of the term is established and agreed upon. However, the opposite is true: What AGI means, or whether it means anything coherent at all, is hotly debated in the AI community. And the meaning and likely consequences of AGI have become more than just an academic dispute over an arcane term. The world’s biggest tech companies and entire governments are making important decisions on the basis of what they think AGI will entail. But a deep dive into speculations about AGI reveals that many AI practitioners have starkly different views on the nature of intelligence than do those who study human and animal cognition—differences that matter for understanding the present and predicting the likely future of machine intelligence.
The original goal of the AI field was to create machines with general intelligence comparable to that of humans. Early AI pioneers were optimistic: In 1965, Herbert Simon predicted in his book The Shape of Automation for Men and Management that “machines will be capable, within twenty years, of doing any work that a man can do,” and, in a 1970 issue of Life magazine, Marvin Minsky is quoted as declaring that, “In from three to eight years we will have a machine with the general intelligence of an average human being. I mean a machine that will be able to read Shakespeare, grease a car, play office politics, tell a joke, have a fight.”
But the reality is that the hard work starts now. The law will enter into force in May, and people living in the EU will start seeing changes by the end of the year. Regulators will need to get set up in order to enforce the law properly, and companies will have between up to three years to comply with the law.
Here’s what will (and won’t) change:
UK-based construction technology firm Automated Architecture (AUAR) has bagged $3.2 million in funding to advance its mission to automate the construction process to solve housing-related issues.
AUAR is creating a network of dispersed micro-factories for environmentally friendly timber homes. AUAR claims that it presents an alternative vision for the built environment, in which local ecosystems comprising developers, contractors, architects, and communities are empowered to construct better homes instead of centralized automation in massive factories.
The LLM Surgeon.
“The LLM Surgeon,” accepted at ICLR 2024, achieves SOTA in LLM pruning in all unstructured, semi-structured, and the most challenging but most effective structured pruning that removes entire matrix rows/columns.
Paper page: https://huggingface.co/papers/2312.17244 Code: https://github.com/notifications/beta/shelf
Contribute to Qualcomm-AI-research/llm-surgeon development by creating an account on GitHub.
Elon Musk’s Neuralink recently implanted a chip in a human for the first time. The emerging market of brain computer interfaces, or BCIs, is in the process of finding its footing. In a world where AI is on the rise, BCIs allow for telepathic control of computers and wireless operation of prosthetics. But how does this tech work?
WSJ goes inside a brain surgery to see how the implants work, and breaks down what it’s going to take to get these devices on the market.
Chapters:
0:00 Musk’s Neuralink.
0:41 The market.
3:03 Synchron.
3:57 Precision.
5:16 What’s next?
News Explainers.
Some days the high-speed news cycle can bring more questions than answers. WSJ’s news explainers break down the day’s biggest stories into bite-size pieces to help you make sense of the news.
#Neuralink #Tech #WSJ