👑 Sundar Pichai's Master Plan to Rule the AI World 🌎

In today’s Tracker:

• 🧪 Research highlights: From Living Organisms to Living Robots: The Rise of Molecular Robotics

• 🚨 Industry news: Google Brains Merge to Dominate AI Race

• ⚖️ Policy and regulation: The Dark Side of Chatbots: UK Lawmakers Sound the Alarm on Risks to Workers

• 🌐 AI and society: AI Models as Tutors: Can They Help Solve Educational Inequalities?

• 🧰 Tool of the day: GPTionary: Word Nerds Rejoice

• 🧠 Let’s get smart with AI: Transferring Intelligence: How AI is Learning to Learn

🧫 From Living Organisms to Living Robots: The Rise of Molecular Robotics

Japan's researchers are working on molecular robots, which are made up of assembled molecules rather than metal and circuits and could be used to cure and diagnose ailments in vivo.

The body, which holds everything together, is the first difficulty in making a molecular robot, and creating complicated structures at the minuscule level has proven challenging.

To overcome this issue, a Tohoku University research team created a simple self-assembly process that employs phospholipids and synthetic surfactants deposited onto a hydrophobic silicone sponge.

Droplets can join to produce heterogeneous structures by employing different sponges with different solutes and producing distinct droplet kinds.

The team believes that this modular approach to construction opens up nearly limitless options and could reshape our perceptions of robots.

🧠 Google Brains Merge to Dominate AI Race

Google has consolidated its artificial intelligence (AI) research teams, integrating Google Research's Brain team and Alphabet's DeepMind.

According to Google CEO Sundar Pichai, the move is intended to speed up AI progress.

DeepMind CEO Demis Hassabis will lead the group. Jeff Dean, who formerly directed Google's AI and research initiatives, will be the chief scientist, building new AI systems with Google Research and DeepMind.

The restructure intends to bring Google's research efforts under one roof and suggests a closer relationship with the rest of Alphabet.

💀 The Dark Side of Chatbots: UK Lawmakers Sound the Alarm on Risks to Workers⚠️

The UK's Business, Energy, and Industrial Strategy Committee has encouraged regulators to look into the possible threats that artificial intelligence (AI) programs, such as OpenAI's ChatGPT, pose to workers.

Based on the paper, regulations may need to be modified to protect workers as technology transforms the workplace.

The research also expressed worries about worker retraining and if present laws adequately safeguard workers' rights and privacy when AI is used in recruitment and surveillance.

According to the paper, regulators should form a task force in collaboration with the Centre for Data Ethics and Innovation and the Trades Union Congress.

👨‍🏫 AI Models as Tutors: Can They Help Solve Educational Inequalities?

Recent improvements in big language models have enabled artificial intelligence (AI) models to assist human teachers, giving a better education for all and aiding in eliminating inequities in the educational system.

However, care must be used because present AI models might be dangerous and hallucinogenic.

Several defenses have been proposed against this, including isolating the model from the learner, utilizing the model to generate learning materials that a human teacher validates, and restricting the model to asking Socratic questions.

The objective is to stay one step ahead of the attackers and defenders in the continuous weapons race.

📝 GPTionary: Word Nerds Rejoice

GPTionary is an AI-powered thesaurus application created by OpenAI that uses the GPT language model to offer synonyms and antonyms for words. The tool is designed to assist users in finding the correct words to use in their writing, whether for artistic reasons, academic writing, or professional communication.

Learn more about the tool here.

💡 Transferring Intelligence: How AI is Learning to Learn

Transfer learning is an AI technique that involves training a model on an extensive dataset for one job and then fine-tuning it on a smaller dataset for a related task.

The goal is to transfer the knowledge learned from the larger dataset to the smaller one, enhancing the model's performance on the new task while requiring less training data.

Transfer learning has grown in popularity in recent years due to its ability to significantly reduce the time and resources required to construct new AI models while maintaining high accuracy.

Using pre-trained models for image recognition or natural language processing tasks and then fine-tuning them for specific applications such as facial recognition or sentiment analysis are popular instances of transfer learning.

🐦 #SocialScene

🌇 Word on the street

I don’t want to scare you, but it was alarming how many people I talked to who are highly placed people in AI who have retreats that are sort of ‘bug out’ houses, to which they could flee if it all hits the fan.

🧠 #MindsetMaze

The mind has a powerful way of attracting things that are in harmony with it, good and bad.

🤪 Meme of the day

🔥 TRENDING TOOLS 🔥

• ✍️ Nichesss - is an AI-powered writing tool that claims to help authors quickly and easily develop blog pieces, social media content, advertising, poems, and even business ideas.

• 📚 Fabled - is an AI-powered writing tool that assists authors in developing and publishing fiction books.

• 🧠 Text Generator Plugin - is an open-source AI helper solution that combines the power of generative AI with the power of knowledge generation and organization in Obsidian.

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