Hi there! 👋

On this edition:

1. From hardware to software: AWS re:Invent was fully focused on AI 🤖

   1. AWS re:Invent: Everything Amazon’s announced, from new AI tools to LLM updates and more (Tech Crunch)

      1. Private AI models, image generation and Amazon Q (AWS’ very own chatbot) took the stage on the line of software.

      2. When it comes to hardware, a lightweight computer to access VMs, palm scanning as ID method and proprietary chips for AI training and modeling were the key topics of the conference ✋

2. The Cybertruck is finally out 🛻

   1. Recap: Tesla delivered its first Cybertrucks to waiting customers in a milestone moment for Elon Musk (Business Insider)

      1. The people that placed their orders back in 2019 will finally receive their futuristic vehicles.

      2. With prices reaching up to $99,990, three versions including a “Cyberbeast“ and actually hardened windows, Musk’s new vehicle is finally on the streets 💰

3. ChatGPT actually wrote a law in Brazil 🇧🇷

   1. Brazilian city enacts an ordinance that was secretly written by ChatGPT (AP)

      1. A councilman in Porto Alegre asked ChatGPT to create a law proposal, the AI processed his prompt and returned an ordinance within seconds. This output was copy-pasted, sent to the rest of the council and approved as-is. [Talk about efficiency!] 🤯

4. Sam Altman offers his support to an startup that makes AI chips Saudi money enters the game, and the US Government did not like it 😠🇺🇸

   1. US compels Saudi fund to exit Altman-backed AI chip startup (Reuters)

      1. The startup Rain AI received $25 million in funding from a subsidiary of Aramco, but the Committee on Foreign Investment in the United States (CFIUS) ordered for this money to be sent back to the Middle East.

      2. One thing to keep in mind is that back in August 2023, the US started a block on the exports of AMD and Nvidia chips developed for AI to the Middle East ❌

5. Time to update Google Chrome in all of your devices 👾

   1. Google Fixes a Seventh Zero-Day Flaw in Chrome—Update Now (Wired)

      1. In order to tackle an exploit that may be seen as spyware related, Google is releasing security patches for the CVE-2023-6345, which points to an integer overflow in the 2D graphics library Skia 🕵️‍♂️

6. 365 days of a wild ride on AI 🎢

   1. ChatGPT is one year old. Here’s how it changed the tech world (Ars Technica)

      1. OpenAI gave LLMs an easy to use UI and public access one year ago. During this time, hundreds of startups have appeared in the market, many others have become obsolete, and the integration of AI in the daily life is more and more tangible 🎂

7. Amazon partners with Space X, stepping over its own space company, Blue Origin 🚀

   1. Amazon taps SpaceX's Falcon 9 rocket to help launch Kuiper satellites (Reuters)

      1. With the goal of deploying over 3 thousand satellites over the planet’s atmosphere, Amazon’s move attempts to build a network that will compete with Space X’s Starlink

      2. Even though Jeff Bezos owns Blue Origin, his company contracted the competitors to help them compete against themselves… Never say never. 🛰️

8. The European Space Agency discovered an amazing astronomical waltz 🪐

   1. Astronomers spot rare star system with six planets in geometric formation (Digital Trends)

      1. The balance of the orbits of this system is so unique that it does not have perturbations, allowing for a mesmerizing geometrical pattern 🤩

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Hi there! 👋

On this edition:

1. Sam Altman, OpenAI and Q* - a thrilling story that just keeps getting more and more exciting 🍿🤖

   1. What is OpenAI Q*? The mysterious breakthrough that could ‘threaten humanity’ (Digital Trends)

      1. Sam Altman trumps OpenAI’s board and is back at the lead of one of the most powerful teams in AI of our time. 😎

      2. Did OpenAI achieve AGI? - Let’s understand hands-on what is Q* and how you can use it 👷🏗️

2. Musk keeps on moving forward trying to connect humans and machines 🧠💻

   1. Neuralink, Elon Musk’s brain implant startup, quietly raises an additional $43M (Tech Crunch)

      1. While the rest of the World is focused in Sam Altman and OpenAI, Elon Musk keeps on hustling in the shadows. 💰

3. Russia is falling behind in AI: Vladimir Putin is not happy about it… 😠🇷🇺

   1. Putin says West cannot have AI monopoly so Russia must up its game (Reuters)

      1. During the Artificial Intelligence Journey 2023 international AI and machine learning conference in Moscow, Putin pushed hard for a much more ambitious Russian strategy for the development of AI: “You cannot ban something - if we ban it then it will develop somewhere else and we will fall behind“. 🚀

4. Anthropic keeps on fighting in the battle of the LLMs 🌦️

   1. Introducing Claude 2.1 (Anthropic)

      1. With a context window of 200K tokens (GPT-4 and Mistral 7b only have 128K), Claude can now process “roughly 150,000 words, or over 500 pages of material“ 🤯

5. GPT-4, Claude, Mistral & Co: Learn all about LLMs from one of the GOATs 🐐

   1. [1hr Talk] Intro to Large Language Models (Andrej Karpathy via YouTube)

6. Stability AI unveils Stable Video Diffusion 🤩

   1. Generative Models by Stability AI (GitHub)

      1. You can now create a video out of an image! Try it now! 🤯

7. AI keeps on pushing the limits: Create a webpage out of a simple screenshot 📸

   1. screenshot-to-code (GitHub)

      1. With this repo you can leverage on the power of GPT-4 Vision and convert a screenshot into a working web page... too much work? - You can also just paste the URL of the website you want to clone and make it your own in seconds 🌠⚡️⭐️

8. Earth was just hit by the second most powerful cosmic ray ever known 🌎

   1. Super high energy particle falls to Earth; its source a mystery (Digital Trends)

      1. The last time that something similar happened, scientists were so in awe that it called it the “Oh-My-God particle“.

      2. This new and little less powerful cosmic ray “had an energy of 2.4 x 1020eV, which is millions of times the energy of the particles produced in a particle collider”. Despite the enormous power of these particles, no one seems to have a clear idea on where do they come from 😱

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Let’s take a deep dive on why Q* might be playing a key role in AI really soon.

• What is AGI?

  • Is there something after AGI?

  • Back to today: What is Q*?

• Q-learning 101

• Hands-on: Q-learning in Python

  • Simple example of Q-learning in Python

  • Q-learning: Practical application in the stock market

What is AGI?

AGI stands for Artificial General Intelligence, which refers to a type of artificial intelligence that has the ability to understand, learn, and apply knowledge in a way that is not specific to a particular task, domain, or narrow set of problems. It is an AI that has the capacity to reason, solve problems, plan, communicate, and integrate knowledge across a wide range of topics as effectively as, or more effectively than, a human being.

Here are some key characteristics often associated with AGI:

1. Learning and Reasoning: AGI can learn from experience and data, reason through problems, and apply logical processes to navigate new situations.

2. Transfer Learning: Unlike narrow AI, AGI can transfer learning from one domain to another. For example, an AGI that learns language could apply its understanding to learn programming.

3. Understanding Context: AGI can understand context and make judgments about how to behave appropriately in different situations, adapting to new environments with little to no additional input.

4. General Problem Solving: AGI is capable of problem-solving across a range of tasks that would typically require human intelligence, such as understanding natural language, recognizing objects, and emotional intelligence.

5. Autonomy: AGI systems can operate independently in a variety of environments and use reason to navigate and make decisions without human intervention.

6. Consciousness and Self-awareness: While still a subject of debate and research, some theories suggest that true AGI would possess some form of consciousness or self-awareness.

As of today, AGI is widely considered as a theoretical concept and has not been achieved. To get some context, current AI systems, including advanced machine learning models and neural networks, are considered narrow AI or ANI (Artificial Narrow Intelligence), meaning they are very good at performing specific tasks but cannot generalize their knowledge and skills to the wide array of tasks that a human can perform. The development of AGI is considered by many to be the holy grail of AI research, but it also raises important ethical, safety, and governance questions.

Is there something after AGI?

The next frontier after AGI is called Superintelligence, and it refers to an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom, and social skills. This concept is most often discussed in the realm of artificial intelligence and is considered an extension of the idea of Artificial General Intelligence (AGI).

The main aspects of superintelligence are:

1. Advanced Cognitive Abilities: A superintelligent AI would be able to outperform the brightest and most gifted human minds.

2. Problem-Solving Skills: Such an AI could solve problems that are currently intractable for humans, potentially finding solutions to complex scientific, mathematical, and social issues.

3. Learning and Adaptation: Superintelligence would have the ability to learn and adapt at an extraordinary pace, far exceeding human cognitive abilities.

4. Technological Mastery: It might have the capability to drive rapid technological growth, potentially leading to the creation of even more advanced forms of AI.

5. Autonomy: A superintelligent system could have the capacity to act autonomously with exceptional proficiency in multiple domains, possibly without human guidance or oversight.

The notion of superintelligence goes beyond existing advanced AI systems, which are designed to excel in specific tasks. The idea carries both optimistic and pessimistic views regarding the future:

- Optimists believe that superintelligence could help solve many of the world's problems, from curing diseases to halting climate change or even traveling to other stars.

- Pessimists worry about the potential risks, including ethical concerns and the possibility that a superintelligent AI could become uncontrollable or make decisions detrimental to human well-being.

Superintelligence is a topic of much speculation, ethical consideration, and philosophical debate, with prominent figures in science and technology discussing its potential impact on humanity. As of now, it remains a hypothetical scenario, with no existing AI systems reaching the levels of capability often described in superintelligence discussions.

Back to today: What is Q*?

In Machine Learning, Q* (pronounced Q-star) is known as the convergence to the optimal action-value function when implementing Q-learning in a Machine Learning model.

Q-learning 101

Q-learning is a type of reinforcement learning algorithm used in machine learning, a subset of artificial intelligence (AI). It's used to find an optimal action-selection policy for any given (finite) Markov decision process. Here's a breakdown of its key aspects:

1. Model-Free Approach: Q-learning is model-free, meaning it doesn't require a model of the environment. It's capable of learning solely from the experience of interacting with the environment.

2. Learning the Q-Value: The core of Q-learning is to learn the value of an action in a particular state. This value (Q-value) represents the expected utility of taking a given action in a given state and following a certain policy thereafter.

3. Bellman Equation: Q-learning uses the Bellman equation to update the Q-values. The equation calculates the expected future rewards that can be obtained from the next state. This allows the algorithm to make decisions that maximize the long-term reward.

4. Exploration vs. Exploitation: In Q-learning, there's a trade-off between exploration (trying new actions to discover their rewards) and exploitation (using actions known to yield high rewards). Balancing these two is crucial for effective learning.

5. Convergence: Under certain conditions, such as having a finite number of states and actions, and with adequate exploration, Q-learning is proven to converge to the optimal action-value function.

6. Applications: Q-learning is widely used in areas such as robotics, automatic control, economics, and gaming. It's particularly effective in situations where the environment is stochastic (randomly determined) and the full model of the environment is unknown.

In practice, Q-learning is implemented using a Q-table or a Q-network (in Deep Q-Learning, which integrates neural networks), where the rows represent the states, the columns represent the actions, and the values represent the expected rewards of taking an action in a state. The algorithm iteratively updates these values as it learns from new experiences.

Hands-on: Q-learning in Python

Simple example of Q-learning in Python

Let's create a simple example of Q-learning in Python. We'll use a very basic environment: a 1D world where the agent must move to the right end to win. This example is simplified to illustrate the concepts clearly.

The environment:

A linear world with 5 states (positions) labeled 0 to 4.

The agent starts at position 0.

The goal is to reach position 4.

Actions are moving left (0) or right (1).

A reward of 100 is given for reaching the goal, and a small negative reward (-1) for each step, to encourage efficiency.

Explanation of the Code:

Initialization: We initialize the environment parameters and the Q-table, which holds the Q-values for each state-action pair.

Action Selection: The choose_action function selects an action for the current state. It uses ε-greedy policy: with probability ε, it explores by choosing a random action, and with probability 1-ε, it exploits the best known action.

Learning: The update function updates the Q-values in the table using the Bellman equation. It takes the current state, next state, reward, and action to adjust the Q-value towards the expected long-term return.

Training Loop: We run multiple episodes where the agent interacts with the environment. In each episode, it starts from the initial state and makes decisions based on its Q-table, updating the table with each step.

Result: After training, the Q-table represents the learned policy, indicating the best action to take in each state.

When you run this code, you'll see the trained Q-table, where each row corresponds to a state and each column to an action. The values represent the expected long-term return for taking that action in that state, guiding the agent to the goal.

Q-learning: Practical application in the stock market

Let's create a more elaborate Q-learning example using an economic scenario. Imagine we have a simplified stock market environment where an agent decides whether to buy, hold, or sell stocks based on their prices.

Scenario Description:

States: The states are a combination of the agent's current portfolio status (e.g., amount of stock owned) and a simplified representation of the market condition (e.g., low, medium, or high prices).

Actions: The agent can choose to buy, hold, or sell stocks.

Rewards: The reward is based on the profit or loss made by the agent's actions. For example, selling at a higher price than the buying price yields a positive reward.

Objective: The agent's goal is to maximize profit over a series of trading days.

*The market conditions and stock prices will be simplified for the sake of the example.

Remember: The Q-table in the output of the Q-learning algorithm represents the learned values (Q-values) for each combination of state and action.

Try it yourself in Google Colab!

Hi there! 👋

On this edition:

1. OpenAI fires Sam Altman and the investors of the company don’t seem happy about it ❌🤖

   1. OpenAI’s board is no match for investors’ wrath (Tech Crunch)

      1. Names like Satya Nadella (CEO of Microsoft) and Vinod Khosla (Khosla Ventures founder) have pledged their support towards Altman, and even considered taking legal actions against OpenAI’ board. 👩🏻‍⚖️💸

2. Facebook says farewell to its Responsible AI division 😰

   1. Facebook-parent Meta breaks up its Responsible AI team (CNBC)

      1. Despite the US Government recently pointing out to the need for better regulations towards AI, Meta teared apart this team reassigning its members to other teams and projects in the company. 🫥

3. Microsoft reveals new products in its yearly It event 🤩

   1. Microsoft Ignite 2023: Copilot AI expansions, custom chips and all the other announcements (Tech Crunch)

      1. Custom AI chips to get rid of the dependency on GPUs, Copilot as competitor for ChatGPT, and Windows AI Studio with direct access to Llama 2 and Stable Diffusion XL are just some of the announcements made by Microsoft on this event. 🤖💻

4. Confirmed that AI is now much better predicting the weather than Europe’s best forecast center 🌦️

   1. Google DeepMind’s AI Weather Forecaster Handily Beats a Global Standard (Wired)

      1. The European Centre for Medium-Range Weather Forecasting (ECMWF)saw its predictions being trumped 90% of the time by DeepMind. What makes this even more interesting is not that this news made it to the Science magazine, but that “the DeepMind model could be run on a laptop and spit out a forecast in under a minute, while the conventional models require a giant supercomputer.“ 🤯⚡️

5. Airbnb keeps moving forward on AI by acquiring a secretive startup 🏡

   1. Airbnb has acquired GamePlanner.AI (Airbnb News)

      1. The startup GamePlanner only has 12 employees, but Airbnb reportedly paid US$ 200 million to add their services under its umbrella. Even though it’s not really clear what does GamePlanner do, this startup was co-founded by Adam Cheyer (Siri, later aquired by Apple) and Siamak Hodjat (Bixby, Samsung’s AI asistant). 💰

6. The IMF keeps on pushing towards Digital Currencies instead of Cash 🪙💸

   1. The Digital Finance Voyage: A Case for Public Sector Involvement (IMF)

      1. With the idea of improving inclusion, and leveraging on the speed of adoption for AI technologies like ChatGPT, Kristalina Georgieva, Managing Director of the International Monetary Fund, took the stage in the Singapore Fintech festival to push policymakers towards the adoption of Cross Border Digital Currencies (CBDCs). 🌎

7. United States vs Google in an antitrust trial 🇺🇸👩🏻‍⚖️

   1. A secret about the riches Apple extracts from Google spills into the open (Yahoo Finance)

      1. Reportedly, Google has been paying Apple 36% of the earnings received via search advertisement via its web browser Safari. Analysts say that this number may add to US$ 20 billion per year, and be one of the reasons why companies like Microsoft can’t integrate their products within Apple’s environment. 📲

8. A practical use case where latency matters 💰⚡️📊

   1. Nasdaq Completes Migration of the Third U.S. Market to AWS (Nasdaq)

      1. Migrating Nasdaq GEMX, one of Nasdaq's six options exchanges to AWS outposts reduces up to a 10% the latency. This is specially important for processing ~12 billion messages daily. The other markets already migrated to the cloud are Nasdaq MRX (2022) and Nasdaq Bond Exchange (NBE) (earlier this year) 🇺🇸📊

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On this edition:

1. OpenAI disrupts the techworld with its first DevDay 🤖

   1. All the news from OpenAI’s first developer conference (The Verge)

      1. GPTs that threaten many AI startups + a GPT store, “Copyright Shield”, and DIY ChatGPT were just some of the few gems showcased by Sam Altman. 💎🤯

2. Alert! Outlook gives Microsoft full access to your mails, passwords and info 🚨

   1. Warning: New Outlook sends passwords, mails and other data to Microsoft (Mailbox)

      1. Latest Outlook versions for Windows, MacOS, iOS and Android make use of a “synchronization” service that push a bit more than metadata to Microsoft’s servers. 🪝

3. You can access Yarn-Mistral-7b-128k from Hugging Face now! 🤗

   1. NousResearch/Yarn-Mistral-7b-128k (Hugging Face)

      1. The acclaimed model Mistral 7b just got a boost to allow for very long context. The first version of the model allowed for an 8k token context window, while this push allows for a 16x larger window (128k, just like the GPT-4 Turbo announced by OpenAI on DevDay), and yes, it comes with the same Apache 2.0 license 😉 ⚡️⚡️⚡️⚡️

4. Barack Obama acknowledges that AI's impact will be a global task 🌎 🤖

   1. Former President Obama Warns 'Disruptive' AI May Require Rethinking Jobs and the Economy (Slashdot)

      1. “The broader question is going to be what happens when 10% of existing jobs now definitively can be done by some large language model or other variant of AI? And are we going to have to reexamine how we educate our kids and what jobs are going to be available...?” 😰

5. Amazon is looking forward to ditch Android and Google with “Vega” 👷🚧🏗️

   1. Amazon Making its Own Linux-Based OS to Replace Android (OMG Ubuntu)

      1. The tech company makes use of old Android versions to bring its Fire TVs, smart displays and other similar devices to life. However, with this new OS codenamed “Vega”, Amazon is trying to move away from their dependency on Google’s Android. 📺

6. Turning off your computer does not mean that the RAM will be pruned 👹

   1. What Is a Cold Boot Attack and Can You Defend Against It? (Make use of)

      1. With help from a bootable USB, attackers may copy the contents of your RAM when your computer restart, in order to copy and get away with your passwords, encryption keys and more information kept in memory. 🥷🔓

7. Google has a new (and very interesting) challenger bringing images to life 🤯

   1. Runway: Introducing, Motion Brush. A new way to add controlled movement to your generations. (X)

      1. A few weeks ago, we were talking about Google Research unveiling Generative Image Dynamics. Now, this company is leveraging Stable Diffusion to the max, in order to create movement on still images by just selecting what to move or create 🤩

8. You can now take a look 10 billion years into the past thanks to Euclid 🌌🔭

   1. ESA releases stunning first images from Euclid, its ‘dark universe detective’ (Engadget)

      1. The European Space Agency is on a mission to create the largest 3D map of the universe until now. Thanks to this, the space telescope Euclid is now showcasing images from galaxies far, far away that will leave you astonished 🤩

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Start your timer… ⏱️ You can create and share your own GPT with the world in less than 5 minutes. Ready?

1. Go to https://chat.openai.com/create

   1. Available only for Plus and Enterprise users, for the time being…

2. Enter the prompt description with the instructions of what you want your GPT to do

   1. Don’t forget to give it a name, enable Web Browsing, DALL·E Image Generation and Code Interpreter if you wish.

3. Click on Save / Publish to: Public and Confirm

   1. Your GPT is alive 🤖⚡️

Check out DevOps GPT here

This analysis aims to compare TensorFlow and PyTorch in terms of their practical use, focusing on aspects such as ease of use, performance, community support, and application in real-world scenarios.

• Wait… what is a Framework?

• TensorFlow & PyTorch: Origins

• Ease of Use

• Performance

• Community and Support

• Real-World Application (with examples)

• Conclusion

Wait… what is a Framework?

A framework in the context of software development is a platform or foundation of pre-written code, which developers can use to build applications or software solutions. It provides a structure and set of guidelines or rules that guide the software development process. Here are key characteristics of a framework:

1. Pre-Defined Structure: Frameworks come with a predefined structure and set of conventions, which help in organizing the code and maintaining consistency across different parts of the application.

2. Reusable Code: They often include libraries of pre-written code, tools, and components that can be reused, saving developers from having to write common functionalities from scratch.

3. Efficiency and Speed: By using a framework, developers can speed up the development process as it automates common programming tasks.

4. Best Practices: Frameworks typically embody best practices in coding and architecture, helping developers to build robust, efficient, and secure applications.

5. Extensibility: They are often designed to be extensible, meaning developers can add their own code or modify the existing code to suit specific project requirements.

6. Community Support: Most frameworks have active communities that contribute to their development, offer support, and create additional tools or plugins.

In different contexts, frameworks can have specific roles. For example, in web development, frameworks like Django (Python) or React (JavaScript) provide tools and libraries for building web applications. In machine learning, frameworks like TensorFlow and PyTorch offer libraries and tools for designing, training, and deploying machine learning models.

TensorFlow & PyTorch: Origins

Developed by Google, TensorFlow excels in large-scale machine learning applications, offering robust performance and scalability. It shines in production environments, leveraging its sophisticated architecture to deliver high efficiency. On the other hand, PyTorch, birthed by Facebook's AI Research lab, champions ease of use and flexibility. It thrives in academic and research settings, offering an intuitive, Pythonic interface that accelerates prototyping and innovation. As key players in the AI realm, TensorFlow and PyTorch shape how we approach and implement machine learning solutions today.

Ease of Use

TensorFlow:

• Initially known for its complex syntax, TensorFlow has evolved with its 2.0 release to offer a more user-friendly interface.

• Its integration with Keras, a high-level neural networks API, has significantly simplified model creation and experimentation.

• TensorFlow provides comprehensive documentation and tutorials, which are beneficial for both beginners and experienced users.

Hello World with TensorFlow:
(From Aymeric Damien via GitHub)

import tensorflow as tf

# Create a Tensor.
hello = tf.constant("hello world")
print(hello)

tf.Tensor(b'hello world', shape=(), dtype=string)

# To access a Tensor value, call numpy().
print(hello.numpy())

b'hello world'

PyTorch:

• PyTorch is often praised for its simplicity and intuitive nature, making it a favorite among researchers and academicians.

• It offers dynamic computation graphs (eager execution), which allows for more flexibility and ease of debugging.

• PyTorch's syntax and style are similar to Python, making it more approachable for Python developers, however, PyTorch doesn't handle string tensors in the same way as TensorFlow. PyTorch is primarily designed for numerical computations and doesn't support string tensors directly.

Numerical tensor with PyTorch*:

import torch

# Create a numerical tensor.
tensor = torch.tensor([1, 2, 3])
print(tensor)

tensor([1, 2, 3])

# To access a Tensor value, call numpy().
print(tensor.numpy())

[1 2 3]

Performance

TensorFlow:

• TensorFlow is known for its high performance in large-scale ML applications. It is highly optimized for GPU and TPU (Tensor Processing Unit) computation.

• It supports distributed training, which is crucial for handling large datasets and complex neural network models.

• TensorFlow’s graph compilation (TensorFlow Graph) can lead to faster execution and better optimization in production environments.

PyTorch:

• PyTorch also offers excellent performance and is well-optimized for GPU acceleration.

• It is preferred for applications requiring rapid prototyping and dynamic computations due to its imperative programming model.

• While PyTorch has made significant strides in distributed training, TensorFlow still holds a slight edge in this area.

Community and Support

TensorFlow:

• TensorFlow, developed by Google, has a vast community and enjoys widespread industry adoption.

• It has a large number of contributors and an extensive ecosystem of tools and libraries.

• TensorFlow’s community provides a wealth of pre-trained models and educational resources.

PyTorch:

• PyTorch, developed by Facebook’s AI Research lab, has rapidly grown in popularity, especially in the research community.

• It has an active and growing community, which contributes to a wide range of libraries and tools.

• PyTorch is often preferred in academia, leading to a wealth of cutting-edge research and papers.

Real-World Application (with examples)

TensorFlow:

• TensorFlow is widely used in industry and production environments, particularly in large-scale ML applications.

• Its robustness and scalability make it suitable for complex applications like image and speech recognition, natural language processing, and more.

• TensorFlow Serving, a system for serving machine learning models, is specifically designed for production environments.

PyTorch:

• PyTorch has gained traction in startups and smaller companies due to its flexibility and ease of prototyping.

• It is extensively used in research and development, with many state-of-the-art models in computer vision and natural language processing being developed using PyTorch.

• PyTorch’s ability to easily switch between eager and graph modes (TorchScript) makes it increasingly viable for production use.

Conclusion

The choice between TensorFlow and PyTorch depends on several factors like project requirements, team familiarity, and the specific application at hand. TensorFlow is often the go-to for large-scale, production-ready applications and projects requiring distributed training. On the other hand, PyTorch offers a more intuitive experience for rapid prototyping, research purposes, and projects where flexibility and ease of use are paramount. Both frameworks are continuously evolving, with each release bridging the gap between research and production, making them both excellent choices for machine learning and deep learning applications.

*The PyTorch examples shared above were refactored from their original TensorFlow versions with help from ChatGPT 4 ❤️🤖🍩

Step into the future of personalized AI with ChatGPT! Creating your own GPT is as simple as starting a conversation. Imagine a digital assistant that knows your preferences, tailors to your tasks, and understands your world - that's the power of GPTs.

Start Simple: Visit chat.openai.com/create and jump into creation mode. No coding, no fuss.

Customize Away: Teach your GPT through conversation. Instructions and knowledge at your fingertips make it unique.

Pick Skills: Choose its abilities - from web searching to image making, your GPT adapts to your needs.

Share the Magic: Release your GPT into the wild. The upcoming GPT Store lets your creation shine and even brings you earnings.

Privacy First: Your chats stay yours. OpenAI respects your data choices, ensuring a secure and private experience.

Connect the Dots: Integrate the real world with APIs. Your GPT becomes your ultimate digital tool.

Build, Enjoy, Repeat: The community awaits your GPT. What will you create today?

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Hi there! 👋

On this edition:

1. LinkedIn boosts your job search with AI 🤖

   1. LinkedIn Wants Its AI Bot to Help Find You a Job After You Lose Your Job to AI (Gizmodo)

      1. Premium members of the platform now have access to a chatbot that through some questions, will help them find the jobs that fit best their interests and experience. 🤝

2. Do you need a digital doppelgänger? 👯

   1. What is a digital twin? And why is everyone suddenly building one? (Fast Company)

      1. Digital twins are very common on industries like manufacturing, where through simulations, a team can infer if, for example, a car can protect its passengers during a high-speed crash. Something similar may happen with human digital twins, giving the opportunity to researchers to identify what may happen to a person under a new medical treatment, or something similar. 🧬

3. New from Apple: faster processors and new computers 🍎

   1. Apple’s October Scary Fast Event: Everything revealed about the new MacBook Pro, iMac and M3 chips (Tech Crunch)

      1. The new M3 processors are supposedly 30% faster than the M1, and this event also meant a farewell to the Touch bar in Mac Book Pros. ⚡️

4. Joe Biden and Kamala Harris aim to place guardrails for AI 👩🏻‍⚖️ 📑

   1. FACT SHEET: President Biden Issues Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence (The White House)

      1. While AI regulations are already moving forward in Europe, the US just started setting up mechanisms for: Creating new safety and security standards for AI, Protecting consumer privacy, Advancing equity and civil rights, and so on. 🇺🇸

5. Elon Musk now has his own LLM and it’s trained on Tweets 😅

   1. Announcing Grok! (Twitter)

      1. Premium X users can ask to join the beta of this conversational model that has real-time knowledge as it’s continuously learning from the X platform. 🤖

6. The Beatles just released a new song thanks to AI 🎧🎸

   1. How Beatles producer Giles Martin used AI to reunite the band for ‘Now and Then’ (Fast Company)

      1. No, no synthetic version of The Beatles was made; the producers used AI to isolate Lennon’s vocals from an old Cassette and use them for a new song. 🎶

7. Is this the end of remote work, or a boost to a work-from-home forever? 💔

   1. WeWork plans to file for bankruptcy as early as next week, source says (Reuters)

      1. This company became super famous for charging the lead for digital nomads, co-working spaces and remote jobs, even getting its own movie in Netflix. What’s next?

8. Are workers as unhappy as ever? 😭

   1. If you hate your job, you’re not alone. Everyone is experiencing the ‘Great Gloom’ (Fast Company)

      1. If more people than ever can work from home or almost anywhere, why do studies point out to a deep low on employee happiness? 📉

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On this edition:

1. Disney is actually into robotics and unveils a real-life WALL-E 🤖

   1. Disney Imagineering’s latest robot looks right out of Star Wars (The Verge)

      1. Out of the Disney Research facilities in Zurich, Switzerland, this mostly 3D printed robot is not only cute, but can also waddle difficult terrain, keep its balance and get back up if kicked down… almost ready to combat the Galactic Empire 👾

2. Now you can create images with generative AI from your Google Search page 🧙‍♂️

   1. New ways to get inspired with generative AI in Search (Google blog - The Keyword)

      1. Way easier to use than Midjourney, no setup necessary like with ComfyUI, and for free 🔮

         1. (Available only in the US, India, and Japan at the moment of writing…🫤)

3. Another victory for Nikola Tesla 😎

   1. I’m Charging My Toothbrush With Wireless Power Over Distance—and It’s a Trip (Wired)

      1. The legendary visionary dreamed of a system for transmitting electricity through the air… 80 years after he left this reality, an Israeli company found a way to turn this idea into a reality. ⚡️

4. Glass is the future of storage 🔎🪞🔮

   1. Sealed in glass (Microsoft Unlocked)

      1. Stone tablets → Paper → Magnetic tapes → HDDs → SSDs → Glass. Full circle 🔄

      2. Microsoft’s Project Silica is using coaster-size glass plates to hold several terabytes of data on each (!), allowing that data to be kept for 10,000 years (!!) and without the need for electricity on any of the storage units (!!!)… the caveat: the data is written and persisted only once. 🤯

5. OpenAI reopens its famous residency program 👩‍🔬

   1. OpenAI Residency (OpenAI)

      1. A 6 month program in San Francisco, with a pay of $210K, unlimited time off, accommodation and Work Sponsorship (if required) included. You’re right, one must be really outstanding to get a spot. ⭐️

6. AI can now predict earthquakes one week before they happen 🌎📈

   1. AI-powered earthquake forecasting proves to be a success (Open Access Government)

      1. With a proven 70% accuracy and a week in advance, this development has the potential to save millions of lives in places like Italy, Mexico, Türkiye and China, where earthquakes have left an infamous mark. ❤️‍🩹

7. Another challenger to the battle of the AI chips 🤖

   1. Microsoft reportedly plans to launch its own AI chip next month (Mashable)

      1. For a long time, Nvidia has dominated the market of AI computing with their even-more scarce GPUs, but under the code-name Athena, the American company may unveil their new processor at Microsoft's Ignite on mid-November. 🚀

         1. Keep in mind that OpenAI is also considering to develop their own AI chips, so with the alliance between Microsoft and the company behind ChatGPT, this could be it.

8. HODL or a Ferrari? 😅 🏎️

   1. Ferrari to accept crypto as payment for its cars in the US (Reuters)

      1. In case you have some digital currency that you are saving for later, this might be a thing to catch your eye. 🤠

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On this edition:

1. Microsoft enters the field of AI Agents with AutoGen 🤖

   1. AutoGen (Github)

      1. “AutoGen is a framework that enables the development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools”. 👾

2. Now you can use LlamaGPT locally 🧑‍💻

   1. LlamaGPT - A Self-Hosted, Offline, ChatGPT (Noted)

      1. The best part is that you can have your own ChatGPT with just ~5 GB of RAM 🚀

3. Python gets a major and interesting upgrade 🐍

   1. Python 3.12.0 (final) now available (Python Insider)

      1. More than just improved error messages…More flexible f-string parsing, support for isolated sub-interpreters, a new debugging profiling and so on.💻

4. Access to OpenAI via a Python SDK 🔮

   1. OpenAI to Release Its Python SDK (Slashdot)

      1. A Beta version that allows you to interact with the OpenAI API right from your Jupyter Notebook or similar has been released, with examples, tips and tricks available at the OpenAI Cookbook. 🧑‍🍳

5. You won’t need contact lenses to use the Vision headset 🤓

   1. Apple’s next Vision headset might ship from the factory with prescription lenses (The Verge)

      1. Is this a good or bad idea? What if my prescription change? 🤨

6. NASA aiming to reach an asteroid ☄️ 🛰️

   1. How to watch NASA’s Psyche mission launch to a metal asteroid this week (Digital Trends)

      1. The rocket launch is happening this Thursday and the goal of the mission is to explore the asteroid which is almost completely made of metal 🔭

7. How war affects AI 😞

   1. Nvidia Cancels Israel AI Summit Over Safety Concerns (Tom’s Hardware)

      1. The event called Nvidia’s AI Summit was scheduled for October 15-16, at EXPO Tel Aviv, and meant to cover topics like Generative AI, Healthcare, Large Language Models, Cybersecurity, Robotics, Omniverse, Autonomous vehicles, and more. 🏳️

8. How much can you trust ChatGPT? 🤨

   1. Google AI Lead: “Don’t take trading advice from ChatGPT” (Crypto News)

      1. Laurence Moroney, AI Lead at Google says that you cannot trust ChatGPT when talking about blockchain technologies. ⛓️🔐

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On this edition:

1. OpenAI enhances the capabilities of ChatGPT 🤖

   1. ChatGPT can now see, hear, and speak (OpenAI)

      1. Now you can have a full conversation with ChatGPT as like you would do with the smartest person you know 🧑‍🏫

2. One step closer to becoming Dr. Dolittle 👨‍⚕️🐶

   1. Artificial Intelligence Could Finally Let Us Talk with Animals (Scientific American)

      1. What if you could actually have a full conversation with your pet? Wouldn’t that be amazing?! 🤩😸

3. The AI battle gets more and more interesting ⚔️

   1. Amazon to invest up to $4 billion in AI startup Anthropic (Tech Radar)

      1. The company behind Claude now has all of the power of AWS to stand up against Google’s Bard and Microsoft + OpenAI’s ChatGPT.💰

4. No news from Google on its event this Wednesday? 📱

   1. Google Pixel event 2023: What to expect from the new Pixel 8 and Watch 2 (Engadget)

      1. The details of the new Pixel phones and smartwatches are already available on their website, so unless they have a very well hidden piece of hardware, or a new software integration to showcase, this event may not be really special 🤨

5. It’s official: Elon Musk is now a contractor of the Pentagon 🚀🛡️

   1. Space Force Taps SpaceX for $70 Million Starshield Contract (Gizmodo)

      1. Starshield is the military satellite network that forks out of Starlink - the satellite internet provider for everyday consumers. 🛰️

6. What if you don’t want your website’s contents to feed the training of Bard, ChatGPT & Co? 🤖🍩

   1. Websites can choose to opt out of Google Bard and future AI models (Mashable)

      1. Google and OpenAI have powerful web crawlers to map the Internet and give answers to all of your prompts, but there might be content that you prefer to keep “private”. 🔒

7. Are chatbots a danger for mindkind? Some people think so 😱

   1. 'Counterfeit people': The dangers posed by Meta’s AI celebrity lookalike chatbots (France 24)

      1. Meta allows you to chat with AIs that resemble personalities of coaches, celebrities and “gurus” like Paris Hilton, Naomi Osaka or Charli D’Amelio; but who controls what those fake personalities tell to their teenager target audience? 🫥

8. That’s what I call a 360˚ immersive experience! - 🤯🤩

   1. The Sphere’s first show looks like it was a mind-blowing spectacle (The Verge)

      1. If the exterior of this building is not already outstanding, the footage of the first performance in this venue is just mind blowing! 🔮

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On this edition:

1. Green light for human tests with Neuralink🚦🔗

   1. Neuralink is recruiting subjects for the first human trial of its brain-computer interface (The Verge)

      1. The first steps on the connection between the human brain and computers 🧠💻

2. UK lawmakers admit that AI is too fast to catch up for them 🥵🏳️

   1. AI developing too fast for regulators to keep up, says Oliver Dowden (The Guardian)

      1. Even though the European Union has been working on the EU AI Act, the British lawmakers urge for a global consensus asap. 📖🌍

3. You can now create additional independent profiles in Facebook 😃🥸😎

   1. Facebook now lets you create alt accounts for better privacy and organization (Tech Radar)

      1. Back in the day one had to use third party tools, now you can easily switch from one profile to the other 🤵🦸‍♂️

4. Amazon’s Kindle Direct Publishing (KDP) vs AI Generated books 🤖📚

   1. AI-generated books force Amazon to cap e-book publications to 3 per day (Ars Technica)

      1. Was your book written by a robot? 🤨 (We all saw that coming…)

5. Cisco is entering the Data Observability business 🔎📊

   1. Cisco to Buy Splunk for $28 Billion in Giant AI-Powered Data Bet (Yahoo Finance)

      1. For decades, Cisco had been a Hardware-first company, but this move puts them in the competition with Datadog and Dynatrace on network health and cybersecurity risks monitoring via a subscription basis 💰

6. Elon Musk planning to charge you for a monthly subscription to use X 🐦

   1. X, formerly Twitter, looks set to become subscription-only (Digital trends)

      1. During a live streaming, the billionaire mentioned that this may be the only way to put a definite stop to the army of bots running in the platform 🤖

7. Mind blowing! Google Research unveil Generative Image Dynamics 🤯

   1. Generative Image Dynamics (Google Research)

      1. Now you can interact with images by clicking and dragging onto them and see them moving (try it yourself! ⬆️ ) 🪄

8. OpenAI started recruiting members for a red team - 🥷🥷🥷

   1. OpenAI Red Teaming Network (OpenAI)

      1. If ChatGPT is not powerful enough already, the company behind it started the hunt for experts in Cybersecurity, Steganography, Mis/disinformation, Political Science, Physics and much more… (Did anyone else immediately thought of Cicada 3301 in steroids?) 😱

BONUS: A 10–15-minute read that’s just too good and matched to our time that’s a MUST.

“The Last Question” by Isaac Asimov.

Heads up: DO NOT read anything about this story before digging in, as even a small spoiler can ruin it.

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Imagine that one day you have the idea to create the next best software; one that might end world hunger, or just be the next big thing on entertainment… but you have just basic knowledge in programming, zero graphic design capabilities and no idea about marketing whatsoever. 😓 Here is exactly where intelligent agents in Artificial Intelligence come into place and get the job done for you.

Just like if you were casting a magic spell, all you need to do is to enter your prompt (the goal), and the AI Agent gets it done for you.

AI Agents: a whole team to your command

With LLMs like ChatGPT, the interaction is step by step, meaning that you enter a prompt or ask a question, and you receive one answer. If you want to refine the answer or dive deeper on the topic, you have to keep on asking and interacting with the AI model. The difference with AI Agents is that you can ask themselves questions, break down complex tasks into multiple streams and validate if each and every step is optimized to the end goal (your initial prompt).

This comes in very handy for cases like the one mentioned above, where you want to develop a piece of software and you need a whole team for programming, testing, marketing, monetizing, and so on. In such scenario, one agent can act as the CEO, other as the programmer, other as QA, other as marketing, other as legal… (you get the point), and interact with each other at every step of the process until all of them deliver the final product. 🤯

One example of this is ChatDev, which is an Open Source project, and according to its creators:

The primary objective of ChatDev is to offer an easy-to-use, highly customizable and extendable framework, which is based on large language models (LLMs) and serves as an ideal scenario for studying collective intelligence.

How does an AI agent work?

Once you define the goal, the AI Agent starts an internal monologue with the LLM and validates its output to create a list of tasks that will be ordered and tackled one by one in order to achieve the final goal.

After the action plan is set in place, the AI Agent starts collecting information online and also from other models to get things done; like ChatGPT asking Dall-E for an image to use as a logo and Jukebox for an audio clip to be the jingle of your marketing campaign.

One important thing to keep in mind is that your AI Agent keeps track of all of the data collected and measures how far is still from the end goal, so it improves autonomously at each iteration based on its previous learnings.

Types of Agents in AI

Now that we know what is an Agent in Artificial Intelligence, let’s take a look at the most common types of AI Agents:

• Goal-based agents:

  • Given a goal, they proactively set a long sequence of actions before deciding if the target was achieved or not, as they don’t always have all the information they need.

• Simple reflex agents:

  • Based on if-then rules, this agents only succeed if they have all the information they need before starting their task.

• Model-based agents:

  • Composed of a Model and an Internal State, this agents act according to the model they are using (like a set of rules) and adapt their state as they understand the result of their actions.

• Utility-Based agents:

  • They keep track of a number as a measure of progress or efficiency, where for each action, the agent keeps track on what is the faster, safer and most convenient way to reach their goal.

• Learning agents:

  • Leveraging on their memory and capability to adapt, these agents challenge themselves through 4 internal components: Learning element, Critic, Performance element and Problem generator. This complexity and feedback loop allows them to outperform the other types of AI agents.

How much does it cost to use and implement an AI Agent?

At the moment of writing, there are limited offerings on the market of AI Agents, but without a question, this niche of AI implementation is set to get a lot more private and open source implementations. We talked about the example of a developer that has the idea of creating an app and starts a group of AI Agents to build, test and market it for them, but this type of technology would fit organically as a keystone to improve the efficiency and revenue of existing companies.

Time to crunch some numbers:

An AI Agent like ChatDev can be downloaded and executed for free, but… it is necessary to map your OpenAI API key in it so it can be used. Let’s take a practical example checking the logs of a Flappy-Bird-like game built with ChatDev:

2023-26-07 12:15:48 INFO] [OpenAI_Usage_Info]

prompt_tokens: 1420

completion_tokens: 500

total_tokens: 1920

• Cost per token (as of September 2023, using GPT-4 with 32K context):

  • Prompt tokens (Input): $0.06 / 1K tokens

  • Completion tokens (Output): $0.12 / 1K tokens

(0.06 x 1.42) + (0.12 x 0.5) = 0.1452 → $0.1452 🤯

Pretty sure this quick calculation makes you want to try it yourself and compare it with the price of what a Freelance developer (or group of developers) may charge you on Fiverr for the same trick, eh?!

Keep in mind that if you are skilled enough, you could tweak the software so instead of using GPT-4 via your OpenAI API Key, it reads a model that you already trained and fine-tuned according to your specific needs, so it could even run in a sandbox and without internet access. This means free, fully private and self improving according to your industry (banking, military, cybersecurity, etc…) 😉

If you find a Colab or repo that does that trick, remember that Sharing is caring ♻️

Conclusion:

AI Agents are like the Tony Stark’s Jarvis of Artificial Intelligence, and even though they are a relatively new technology, they already have the power to develop and optimize software like if that’s a simple task. How would you use an AI Agent today?

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On this edition:

1. Apple reveals the iPhone 15📱

   1. Apple just announced the iPhone 15 — here are the key new features and how to preorder (Business Insider)

      1. Yeah, I know… but this time it comes with USB-C and you can also get it with a Titanium frame 💸

2. Gemini is getting closer to public release ♊️

   1. Google nears release of AI software Gemini, The Information reports (Reuters)

      1. Much more than just text. BARD leverages reinforcement learning techniques to optimize model architectures and hyperparameters, while Gemini utilizes generative adversarial networks (GANs) for high-fidelity image synthesis. 🤖

3. Attention Mac users! MetaStealer is on the loose 🚨👾

   1. This dangerous new Mac malware steals your credit card info (Digital Trends)

      1. A DMG file inside a PDF that can read the Mac’s Keychain (logins, passwords, credit cards info, encryption keys, etc…) 👹

4. The US Government released a guide on how to identify Deepfakes 🥸

   1. Contextualizing Deepfake Threats to Organizations (Defense.gov)

      1. Alex Syvitski put together a short and concise explanation to the 18-page document published by the NSA, FBI and CISA… Highly recommended! ➡️ Basic Guide to Detect Deepfake (Alex Syvitski via LinkedIn) 🤓

5. Meet the $800K / year engineer 💰💰💰

   1. How I Became An $800K Meta Staff Engineer (Taro)

      1. Skill, consistency and “some luck“ made this guy go from the not-so-despicable $390K to an amazing $823K/year working at Meta (Facebook) in 4.5 years 🤯

6. The Polish drinks company Dictador appointed a humanoid as their CEO 🤖🥃

   1. The humanoid robot CEO of a drinks company says she doesn't have weekends and is 'always on 24/7' (Yahoo Finance)

      1. Mika will help the company leverage on her AI brain for complex tasks, but still human executives will analyze and execute big decisions. Definitely a great boost in Marketing for the spirits-producer 🤑

7. How urgent is it to include Generative AI into your business? 🚥⏰

   1. Where Should Your Company Start with GenAI? (Harvard Business Review)

      1. Map your business into a decision matrix to identify how big would be its impact in it 🔮

8. Expert penetration tester explains on simple words how to do the trick 🥷

   1. Hacker Answers Penetration Test Questions From Twitter | Tech Support | WIRED (WIRED via YouTube)

      1. Just like the link you just clicked tells me who you are and how you landed into my newsletter, this expert explains why you should not click just any link on the Internet 😳

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On this edition:

1. Care about your Privacy? Better stick to your old car or bicycle! 🚗 🚲

   1. It’s Official: Cars Are the Worst Product Category We Have Ever Reviewed for Privacy (Mozilla)

      1. From where you live to your sex life… your new car knows everything about you 😱

2. OpenAI and the $80 million / month business 🤑

   1. OpenAI Passes $1 Billion Revenue Pace as Big Companies Boost AI Spending (The Information)

      1. After a $27 billion investment from Microsoft, ChatGPT is OpenAI’s cash cow 🐮

3. Elon Musk vs The State of California ⚖️ 🥊

   1. Elon Musk’s X sues California over law it says forces social media services to block posts ‘viewed by the state as problematic’ (Fortune)

      1. Is the billionaire looking for trouble or freedom?

4. The Software Update that stopped an entire airline in the US ❌ ✈️

   1. United Airlines says the outage that held up departing flights was not a cybersecurity issue (Associated Press)

      1. In the end it was just a contractor playing around with the backups 🙈

5. Will Claude Pro dethrone ChatGPT? 🤖 👑

   1. The AI-assistant wars heat up with Claude Pro, a new ChatGPT Plus rival (Ars Technica)

      1. 5x higher usage limits, 100,000 vs ‘only’ 8,000 tokens and a very familiar $20 / month subscription 🤔

6. Airbnb to be wiped off New York 🫥🗽

   1. The End of Airbnb in New York (Wired)

      1. Due to the Local Law 18, New Yorkers can only have up to two short-term guests if and only if they are present when their visitors are in town 💸

7. If your software may be hackable, the Chinese Government must know how 🔎 🇨🇳

   1. Sleight of hand: How China weaponizes software vulnerabilities (Atlantic Council)

      1. The US Government already has a CVEs database (NVD), but China wants them mapped to every software who has them 🚪

8. Wired or Hacked? The UK elections at risk 🔥 🇬🇧

   1. UK election body failed cybersecurity test before hack (Cybernews)

      1. Right after leaking the data of ~40 million voters, the UK’s Electoral Commission is still open to attacks 🐀

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