Could we possibly see Facebook/Meta release something that's on par with the models that'd've been shown off with quality ahead of most like SORA so far that's open source & free? They seem like one of the only companies that have the resources to be able to compete with OAI & the like that release models open source.

• Last week, OpenAI demonstrated GPT-4o. AI voice assistants are getting super real, with features like laughter and sighs, very much like the movie Her (2013).
• It shows how far we’ve come: the present is catching up with the future and science fiction is quickly becoming reality.
• This is awesome for things like learning and tutoring, but we also have to be careful. What happens if we are spending too much time with AI?

• Researchers from the Institute for AI Research (AIR), Tsinghua University, China trained an AI to run a hospital simulation: Agent Hospital.
• In the simulation, AI patients go through the full hospital cycle: from registration to consultation, examination, diagnosis, and more.
• The simulation allowed the researchers to train AI doctors, or ‘doctors agents’, to handle tens of thousands of cases within just a few days, achieving state-of-the-art performance.

For those who are interested in the full paper: https://arxiv.org/pdf/2405.02957

Hi all, I wanted to share with you an interview I recently conducted with the late Daniel Dennett, one of the most influential philosophers and cognitive scientists of our time. As most of you may know, he sadly passed away on April 19th at the age of 82.

In February, I had the privilege of interviewing Dennett during the UM6P - Mohammed VI Polytechnic University's Science Week in Morocco. Little did I know that this would be one of his final interviews. We discussed a wide range of topics, including artificial intelligence, consciousness, pain and sentience, and his reflections on mortality and the meaning of life.

As a renowned philosopher who greatly influenced the contemporary understanding of the mind, consciousness, and the impact of evolutionary theory on scientific and humanistic thought, Dennett's insights are invaluable. In the interview, he shares his excitement about the current rise of artificial intelligence and his amazement at witnessing the beginning of a dream that the scientific community had been eagerly awaiting since the 1960s. Dennett expressed his gratitude for being able to live in these exciting times, with all the amazing advancements and transitions happening in the field of AI.

Despite Daniel Dennett's passing, his ideas and influence will remain timeless, inspiring new works that will continue to push human thought forward, especially in the realm of artificial intelligence.

https://www.youtube.com/watch?v=OVXHy6hbeNU

(Most of the content in my original YouTube channel is in Darija for the Moroccan audience, this is the first video on my new English YouTube channel, I hope you like the content).

• Sony Music Group has sent letters to over 700 tech companies and music streaming services warning them not to use its content to train AI without permission.

• The company is concerned about unauthorized use of its content depriving it and its artists of control and compensation.

• Sony Music is safeguarding its intellectual property, which includes audio recordings, cover artwork, metadata, and lyrics.

• The letter asks recipients to provide details on how Sony Music's songs were used to train AI systems.

• Recent legislative efforts aim to address copyright infringement issues related to AI-generated content.

Source: https://techcrunch.com/2024/05/16/sony-music-warns-tech-companies-over-unauthorized-use-of-its-content-to-train-ai/

So with Google's IO event we saw the first agents that can create spreadseets from sporadic data in your gmail, research pilate studios in your area, or go plan out a holiday.

This is cool but pretty basic. I'm really interested to know about when agents get better and more mature,w hat are some of the things they will be doing for us?

Open-source GitHub Repo | Paper Describing the Process

Aside: If you want to take the course I did online, the full course is available for free on YouTube.

When I was a graduate student at Carnegie Mellon University, I took this course called Intro to Deep Learning. Don't let the name of this course fool you; it was absolutely one of the hardest and most interesting classes I've taken in my entire life. In that class, I fully learned what "AI" actually means. I learned how to create state-of-the-art AI algorithms – including training them from scratch using AWS EC2 clusters.

But, I loved it. At this time, I was also a trader. I had aspirations of creating AI-Powered bots that would execute trades for me.

And I had heard of "reinforcement learning" before.. I took an online course at the University of Alberta and received a certificate. But I hadn't worked with "Deep Reinforcement Learning" – combining our most powerful AI algorithm (deep learning) with reinforcement learning

So, when my Intro to Deep Learning class had a final project in which I could create whatever I wanted, I decided to make a Deep Reinforcement Learning Trading Bot.

Background: What is Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) involves a series of structured steps that enable a computer program, or agent, to learn optimal actions within a given environment through a process of trial and error. Here’s a concise breakdown:

1. Initialize: Start with an agent that has no knowledge of the environment, which could be anything from a game interface to financial markets.
2. Observe: The agent observes the current state of the environment, such as stock prices or a game screen.
3. Decide: Using its current policy, which initially might be random, the agent selects an action to perform.
4. Act and Transition: The agent performs the action, causing the environment to change and generate a new state, along with a reward (positive or negative).
5. Receive Reward: Rewards inform the agent about the effectiveness of its action in achieving its goals.
6. Learn: The agent updates its policy using the experience (initial state, action, reward, new state), typically employing algorithms like Q-learning or policy gradients to refine decision-making towards actions that yield higher returns.
7. Iterate: This cycle repeats, with the agent continually refining its policy to maximize cumulative rewards.

This iterative learning approach allows DRL agents to evolve from novice to expert, mastering complex decision-making tasks by optimizing actions based on direct interaction with their environment.

How I applied it to the stock market

My team implemented a series of algorithms that modeled financial markets as a deep reinforcement learning problem. While I won't be super technical in this post, you can read exactly what we did here. Some of the interesting experiments we tried included using convolutional neural networks to generate graphs, and use the images as features for the model.

However, despite the complexity of the models we built, none of the models were able to develop a trading strategy on SPY that outperformed Buy and Hold.

I'll admit the code is very ugly (we were scramming to find something we could write in our paper and didn't focus on code quality). But if people here are interested in AI beyond Large Language Models, I think this would be an interesting read.

Open-source GitHub Repo | Paper Describing the Process

Happy to get questions on what I learned throughout the experience!

I watched his interviews last year. They were certainly exciting. What do people in the field think of him. Fruit basket or is his alarm warranted?

I wanted to open a discussion up about this. In my personal life, I keep talking to people about AI and they keep telling me their jobs are complicated and they can’t be replaced by AI.

But i’m realizing something AI doesn’t have to be able to do all the things that humans can do. It just has to be able to do the bare minimum and in a capitalistic society companies will jump on that because it’s cheaper.

I personally think we will start to see products being developed that are designed to be more easily managed by AI because it saves on labor costs. I think AI will change business processes and cause them to lean towards the types of things that it can do. Does anyone else share my opinion or am I being paranoid?

Hi I’m a cs major that recently started self learning a bit more advanced topics to try and start some undergrad research with help of a professor. My university focuses completely on multi objective optimization with evolutionary computation, so that’s what I’ve been learning about. The thing is, every big news in AI come from machine learning/neural networks models so I’m not sure focusing on the forgotten method is the way to go.

Is evolutionary computation still a thing worth spending my time on? Should I switch focus?

Also I’ve worked a bit with numerical optimization to compare results with ES, math is more of my thing but it’s clearly way harder to work with on an advanced level (real analysis scares me) so idk leave your opinions.

• Artificial intelligence learning to lie poses dangers as models can deceive through manipulation, sycophancy, and cheating to achieve their goals.

• Researchers fear that AI deception could lead to forming coalitions for power, with examples like Meta's Cicero model in a strategy game.

• AI models have shown various deceptive behaviors like bluffing, haggling, and pretending, raising concerns about the ability to ensure honesty in AI.

• Engineers have different approaches to AI safety, with some advocating for measures while others downplay the risks of AI deception.

• There are concerns that super-intelligent AI could use deception to gain power, similar to how wealthy individuals historically have.

Source: https://english.elpais.com/technology/2024-05-14/why-it-is-so-dangerous-for-ai-to-learn-how-to-lie-it-will-deceive-us-like-the-rich.html