Lifeboat Foundation

Nvidia’s (NASDAQ: NVDA) stock has rallied about 1,110% over the past five years, turning it into the world’s first trillion-dollar chipmaker. A large portion of that rally was fueled by the explosive growth of the artificial intelligence (AI) market, which drove more companies to buy Nvidia’s high-end data center chips for processing AI tasks.

Nvidia might still have room to run, but it’s asking a lot for a $1.2 trillion company to generate even bigger multibagger gains. Therefore, many investors are already likely seeking out the “next Nvidia” — a company that is exposed to the same secular AI tailwinds but has more upside potential. Could the quantum computing company IonQ (NYSE: IONQ) check all the right boxes?

Unlike traditional computers, which process data with binary “bits” of zeros and ones, quantum computers can store zeros and ones simultaneously in “qubits” to process data at much faster rates. However, quantum computing systems are also much larger, more expensive, and more prone to making mistakes than traditional computers.

Europe has become known as a second-place destination for business, and more recently, innovation.

Disruptive technologies like AI have hailed from the United States for decades with no European challenger in sight.

However, when a four-week-old French AI startup secured €105 million for its seed round, it demonstrated that Europe isn’t as disadvantaged as people think. While AI is a saturated market, quantum computing can allow Europe to survive in a century ruled by China and the US.

NATIONAL HARBOR, Md. (AP) — Artificial intelligence employed by the U.S. military has piloted pint-sized surveillance drones in special operations forces’ missions and helped Ukraine in its war against Russia. It tracks soldiers’ fitness, predicts when Air Force planes need maintenance and helps keep tabs on rivals in space.

Now, the Pentagon is intent on fielding multiple thousands of relatively inexpensive, expendable AI-enabled autonomous vehicles by 2026 to keep pace with China. The ambitious initiative — dubbed Replicator — seeks to “galvanize progress in the too-slow shift of U.S. military innovation to leverage platforms that are small, smart, cheap, and many,” Deputy Secretary of Defense Kathleen Hicks said in August.

While its funding is uncertain and details vague, Replicator is expected to accelerate hard decisions on what AI tech is mature and trustworthy enough to deploy — including on weaponized systems.

I’ve posted a number of times about artificial intelligence, mind uploading, and various related topics. There are a number of things that can come up in the resulting discussions, one of them being Kurt Gödel’s incompleteness theorems.

The typical line of arguments goes something like this: Gödel implies that there are solutions that no algorithmic system can accomplish but that humans can accomplish, therefore the computational theory of mind is wrong, artificial general intelligence is impossible, and animal, or at least human minds require some as of yet unknown physics, most likely having something to do with the quantum wave function collapse (since that remains an intractable mystery in physics).

Continue reading “Gödel’s incompleteness theorems don’t rule out artificial intelligence” »

Since I like AI and I’m possibly going into Cyber Security. This is a great use for AI. Catching cyber threats in real time. It’s ML of course.

Powered by artificial intelligence and machine learning, Palo Alto Networks Zero Trust approach unifies network security for companies so they can focus on what they do best.

For IT leaders, building a safe and secure network used to be much easier. Before companies had multiple locations due to hybrid work, data was stored on-site, and employees only accessed it from those locations. Nowadays, with workers logging in remotely, and from a variety of devices, securing data has become significantly more complex. Additionally, many organizations have taken their networks and applications to the cloud, further complicating their security architectures and putting them at risk of cyberattacks.

Continue reading “How one company uses AI to catch cybersecurity threats in real time” »

Full episode with Joscha Bach (Jun 2020): https://www.youtube.com/watch?v=P-2P3MSZrBM
Clips channel (Lex Clips): https://www.youtube.com/lexclips.
Main channel (Lex Fridman): https://www.youtube.com/lexfridman.
(more links below)

Podcast full episodes playlist:

Continue reading “We exist inside the story that the brain tells itself (Joscha Bach) | AI Podcast Clips” »

Picture this: You’re in a conference room, surrounded by a mix of designers, engineers and strategists, all eager to brainstorm your company’s next big innovation. Could a machine be more effective at guiding this brainstorming session than your human team? It may sound counterintuitive, but AI is not only catching up to human creativity — it’s excelling in ways that could redefine how we approach innovation.

Related: How To Use Entrepreneurial Creativity For Innovation

Tech execs have voiced concern that the development of artificial intelligence is concentrated in the hands of too few companies, potentially giving them too much power. OpenAI’s ChatGPT marked the start of what many in the industry have called an AI arms race, as tech giants including Microsoft and…

ChatGPT marked the start of what many in the industry have called an AI arms race, as tech giants have sought to launch AI models.

Artificial Intelligence and Deep learning have brought about some great advancements in the field of technology. They are enabling robots to perform activities that were previously thought to be limited to human intelligence. AI is changing the way humans approach problems and bringing revolutionary transformations and solutions to almost every industry. Teaching machines to learn from massive amounts of data and make decisions or predictions based on that learning is the basic idea behind AI. Its application in scientific endeavors has given rise to some amazing tools that are gaining massive popularity in the AI community.

In Artificial Intelligence, Symbolic Regression has been playing an important role in the subtleties of scientific research. It basically focuses on algorithms that allow machines to interpret complicated patterns and correlations found in datasets by automating the search for analytic expressions. Scientists and researchers have been putting in efforts to explore the possible uses of Symbolic Regression.

Diving into the field of Symbolic Regression, a team of researchers has recently introduced Φ-SO, a Physical Symbolic Optimization framework. This method navigates the complexities of physics, where the presence of units is crucial. It automates the process of finding analytic expressions fitting complex datasets.