Every week, my team at Invector Labs publishes a newsletter to track the most recent developments in AI research and technology. You can find this week’s issue below. You can sign up for it below. Please do so, our guys worked really hard on this:

From the Editor: Can AI Systems Be Conscious?

Consciousness is one of the legendary arguments in the field of artificial intelligence. Can machines be conscious? Would AI agents be able to think or just simulate thinking? Defining consciousness is a debate in and out itself but most of us associate consciousness with our ability to be aware of our mental state. However, that definition is still too generic to apply to AI systems.

In order to be applicable to AI, a theory of consciousness needs to be more pragmatic and technical and less, let’s say, philosophical. My favorite definition of consciousness that follows these principles comes from the laureate physicist Michio Kaku, professor of theoretical physics at University of New York and one of the creators of string theory. In his theory, Dr. Kaku differentiates different levels of consciousness for different species. For instance, the level of consciousness of species like reptiles is reduced to space while mammals can create very strong models of the world based on their relationship to other species. The ultimate level of consciousness is human’s ability to imagine the future. Using Dr. Kaku’s definition, then you can make the case that AI systems have some level of consciousness but far from achieving human-level consciousness. Fascinating topic.

Now let’s take a look at the core developments in AI research and technology this week:

AI Research

Uber published a research paper proposing a method to better quantify the effectiveness of training processes.

>Read more in this blog post from the Uber engineering team

Google AI researchers published a paper that outlines a “sketching” technique to summarize how neural networks are understanding data inputs.

>Read more in this blog post from Google Research

Continuing with Google, the search giant also published two papers proposing a self-supervised learning method for vide data.

>Read more in this blog post from Google Research

Cool AI Tech Releases

Apple unveiled their work on Overton, a new platform for monitoring and improving machine learning systems.

>Read more in this coverage from VentureBeat

Uber is one of the most active contributors to open source AI technologies. The Uber engineering team recently host a meetup to share some of their work.

>You can find the sessions in this blog post from the Uber engineering team

AI in the Real World

The Partnership on AI(PAI) consortium published a paper addressing the challenges of AI researchers obtaining VISAS to travel to conferences.

>Read more in this blog post from the PAI team

AI-powerhouse ElementAI announced that it has raised $151 million to continue the implementation of its AI products.

>Read more in this coverage from TechCrunch

Researchers from the Massachusets Institute of Technology(MIT) built a system called RiskCardio that uses AI to estimate the risk of cardiovascular death on a patient.

>Read more in this coverage from MIT News

CEO of IntoTheBlock, Chief Scientist at Invector Labs, I write The Sequence Newsletter, Guest lecturer at Columbia University, Angel Investor, Author, Speaker.

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