Lifeboat Foundation: Safeguarding Humanity
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X’s new privacy policy, which is due to come into effect on September 29, states that the company “may use the information we collect and publicly available information to help train our machine learning or artificial intelligence models for the purposes outlined in this policy.” This policy is not included in its previous terms, which are still posted online.

Musk responded to a post about this change on X, saying that it would only use publicly available information to train the AI and would not use “DMs or anything private.”

During a live audio session on X – formerly Twitter – in July, Elon Musk said that his AI startup, xAI, would use public data from his social media platform to train its AI models. Insider reached out to X for comment but didn’t immediately hear back. It is not clear how it will use the information from X and which AI models this relates to.

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In what can only bode poorly for our species’ survival during the inevitable robot uprisings, an AI system has once again outperformed the people who trained it. This time, researchers at the University of Zurich in partnership with Intel, pitted their “Swift” AI piloting system against a trio of world champion drone racers — none of whom could best its top time.

Swift is the culmination of years of AI and machine learning research by the University of Zurich. In 2021, the team set an earlier iteration of the flight control algorithm that used a series of external cameras to validate its position in space in real-time, against amateur human pilots, all of whom were easily overmatched in every lap of every race during the test. That result was a milestone in its own right as, previously, self-guided drones relied on simplified physics models to continually calculate their optimum trajectory, which severely lowered their top speed.

This week’s result is another milestone, not just because the AI bested people whose job is to fly drones fast, but because it did so without the cumbersome external camera arrays= of its predecessor. The Swift system “reacts in real time to the data collected by an onboard camera, like the one used by human racers,” an UZH Zurich release reads. It uses an integrated inertial measurement unit to track acceleration and speed while an onboard neural network localizes its position in space using data from the front-facing cameras. All of that data is fed into a central control unit — itself a deep neural network — which crunches through the numbers and devises a shortest/fastest path around the track.

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The Guardian has blocked OpenAI from using its content to power artificial intelligence products such as ChatGPT. Concerns that OpenAI is using unlicensed content to create its AI tools have led to writers bringing lawsuits against the company and creative industries calling for safeguards to protect their intellectual property.

The Guardian has confirmed that it has prevented OpenAI from deploying software that harvests its content.

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As humanity’s gaze turns towards the stars, one name stands at the forefront of the space exploration revolution: Elon Musk’s SpaceX. Among its many ambitious projects, the SpaceX Starship promises to reshape our understanding of interplanetary travel. This colossal 9-meter diameter rocket has captured our imagination with the grand vision of shuttling thousands of people on a six-month journey to Mars. But what lies within this futuristic vessel? What can we expect from the spaceship interior that aims to make long-duration space travel a reality?

Historically, our mental image of a spacecraft has often been based on cramped capsules, such as the iconic Apollo 11, Soyuz, or Dragon. These designs, while functional, have offered little in the way of comfort. Even modern incarnations like the Orion spacecraft still lack the headroom to stand upright inside the Command Module. With its larger size, the space shuttle hinted at more livable conditions, but it still fell short of providing ample space for extended journeys.

Enter the SpaceX Starship—a towering, 9-meter diameter rocket that evokes images of Flash Gordon’s futuristic transport. Elon Musk’s vision of a vessel capable of shuttling thousands to Mars within six months is a compelling proposition. However, spending half a year in the confined space of a metal box hurtling through an interplanetary void is daunting, even if the box is quite spacious. As we anticipate the Starship interior, our expectations are high, and speculation runs rampant about what life onboard might entail.

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Artificial Intelligence has transformed how we live, work, and interact with technology. From voice assistants and chatbots to recommendation algorithms and self-driving cars, AI has suddenly become an integral part of our daily lives, just a few months after the release of ChatGPT, which kickstarted this revolution.

However, with the increasing prevalence of AI, a new phenomenon called “AI fatigue” has emerged. This fatigue stems from the overwhelming presence of AI in various aspects of our lives, raising concerns about privacy, autonomy, and even the displacement of human workers.

AI fatigue refers to the weariness, frustration, or anxiety experienced by individuals due to the overreliance on AI technologies. While AI offers numerous benefits, such as increased efficiency, improved decision-making, and enhanced user experiences, it also presents certain drawbacks. Excessive dependence on AI can lead to a loss of human agency, diminishing trust in technology, and a feeling of disconnection from the decision-making process.

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Michael Levin is a Distinguished Professor in the Biology department at Tufts University. He holds the Vannevar Bush endowed Chair and serves as director of the Allen Discovery Center at Tufts and the Tufts Center for Regenerative and Developmental Biology. To explore the algorithms by which the biological world implemented complex adaptive behavior, he got dual B.S. degrees, in CS and in Biology and then received a PhD from Harvard University. He did post-doctoral training at Harvard Medical School, where he began to uncover a new bioelectric language by which cells coordinate their activity during embryogenesis. The Levin Lab works at the intersection of developmental biology, artificial life, bioengineering, synthetic morphology, and cognitive science.

✅EPISODE LINKS:
👉Round 1: https://youtu.be/v6gp-ORTBlU
👉Mike’s Website: https://drmichaellevin.org/
👉New Website: https://thoughtforms.life.
👉Mike’s Twitter: https://twitter.com/drmichaellevin.
👉Mike’s YouTube: https://youtube.com/@drmichaellevin.
👉Mike’s Publications: https://tinyurl.com/yc388vvk.
👉The Well: https://www.youtube.com/watch?v=0a3xg4M9Oa8 & https://youtu.be/XHMyKOpiYjk.
👉Aeon Essays: https://aeon.co/users/michael-levin.

✅TIMESTAMPS:
0:00 – Introduction.
1:27 – The Prisoner’s Dilemma (Game Theory applied to Life)
7:55 – Computational Boundary of the Self.
10:17 – “Goal States” & “Cognitive Light Cones”
13:55 – To Naturalise Cognition.
19:00 – The Hard Problem of Consciousness.
23:10 – Defining Consciousness.
27:14 – The Field of Diverse Intelligence.
43:25 – Who inspired Mike within his field.
46:52 – Is Mike a Panpsychist?
52:09 – Thoughts on Illusionism.
55:44 – Links to IIT
57:56 – Technological Approach to Mind Everywhere (TAME 2.0)
1:02:14 – Proof of Humanity Certification.
1:10:00 – Phase Transitions in Mathematics.
1:15:26 – Bioelectric Medicine.
1:21:06 – Can Cells Think? What is the Self? Is Man a Machine?
1:28:55 – Metacognition & Cloning.
1:35:49 – Teleology, Teleonomy & Teleophobia.
1:50:08 – All Intelligence is Collective Intelligence.
1:54:33 — Conclusion.

Video Title: What is The Field of Diverse Intelligence? Hacking the Spectrum of Mind & Matter | Michael Levin.

🔔Ready to change the way you think about the mind-body dichotomy? Join Dr. Tevin Naidu on a quest to conquer the mind-body problem. Subscribe and take one step closer to the Mind-Body Solution: https://t.ly/ASNw6

⭐ ⭐ ⭐ ⭐ ⭐ Audio Podcast is currently on your favorite platforms:

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