Latest AI and Machine Learning News - Funding, Research Breakthroughs, and More

Table of Contents

• Anthropic Raises $124 Million to Develop Steerable, Reliable AI Models
• DeepMind Introduces Android Learning Environment for AI Research
• Most Executives Don't Understand How Their AI Models Make Decisions
• Academic Collusion Rings Threaten Integrity of AI Conferences
• Original ELIZA Chatbot Source Code Discovered
• OpenAI Launches $100M Fund to Support Positive-Impact AI Startups

Anthropic Raises $124 Million to Develop Steerable, Reliable AI Models

Anthropic, a new AI research company founded by Dario Amodei of OpenAI and his sister Daniela Amodei, has raised $124 million in a Series A funding round. The round was led by Jan Talinn, the co-founder of Skype, and also included investors such as Eric Schmidt and Dustin Moskovitz.

According to Anthropic's press release, the company's goal is to make fundamental research advances that will enable the development of more capable, general, and reliable AI systems. The research principles are centered around AI safety, developing tools to measure progress, and ensuring benefits to society.

The mission and principles sound very similar to OpenAI's original goals of developing AI that is evenly distributed and benefits humanity. However, unlike the non-profit OpenAI, Anthropic is a for-profit company and investors likely expect financial returns.

Mission Aligns with Original Aims of OpenAI

If you look back at OpenAI's first blog post introducing itself, the mission sounds very similar - promoting widely distributed benefits for AI. While OpenAI is non-profit, Anthropic presumably intends to build profitable ventures in the long run.

Profitable Ventures Expected Despite Non-Profit Vibes

So while Anthropic seems to have ideals reminiscent of early OpenAI, the company will need to eventually monetize its work, even if the initial focus is on research. When OpenAI previously released APIs and models openly, Anthropic may follow more of a commercial path.

DeepMind Introduces Android Learning Environment for AI Research

DeepMind has released the Android Learning Environment, building on the Android emulator to enable reinforcement learning research on Android apps. The environment provides unified descriptions of app interfaces to allow training intelligent agents.

There are many possibilities for Android ALE. It facilitates multitask training across apps, learning visual perception from screen images, and exploring how much can be achieved without hand-engineered policies. Interacting with real-world Android apps could provide a smoother transition from toys tasks to applications like robotics.

Most Executives Don't Understand How Their AI Models Make Decisions

A new survey from FICO and Corinium found that 65% of 100 surveyed executives could not explain how their company's AI models work internally. While concerning, it is not surprising that business executives might lack technical understanding of their AI systems.

Concerning Lack of Explainability

The survey highlights an important issue - lack of explainability in AI systems limits accountability. Understanding why models make certain predictions is critical, especially for business applications.

Academic Collusion Rings Threaten Integrity of AI Conferences

In an article in Communications of the ACM, Michael L. Littman warns of "collusion rings" that threaten the peer review process at academic conferences. A collusion ring involves a group of researchers secretly coordinating to peer review each other's work positively and lobby for acceptance, undermining paper quality.

Original ELIZA Chatbot Source Code Discovered

The original 1966 source code for ELIZA, Joseph Weizenbaum's famous early natural language processing chatbot, has been found in MIT's archives. The code reveals ELIZA's simple pattern matching techniques, which sparked early excitement but could not maintain coherent conversations.

Code Reveals Simple Pattern Matching Techniques

The ELIZA code consists mainly of pattern substitutions based on keywords, reflecting user input back at them. This technique was inspired by person-centered therapy but did not capture true understanding.

Rogers' Methods More Sophisticated Than ELIZA

While ELIZA was based on Carl Rogers's therapeutic methods, it was an oversimplified parody. Rogers' careful restating of patient opinions and reflections demonstrated far more advanced conversation skills that AI has struggled to achieve even today.

OpenAI Launches $100M Fund to Support Positive-Impact AI Startups

OpenAI has announced a $100 million startup fund dedicated to backing new companies aiming to positively impact society through AI. The fund will invest in a small number of early-stage startups in healthcare, climate, education, and other areas.

FAQ

Q: How much funding did Anthropic raise?
A: Anthropic raised $124 million in a Series A funding round.

Q: What is the Android Learning Environment?
A: The Android Learning Environment is a new platform from DeepMind for training AI agents to interact with Android apps via reinforcement learning.

Q: What percentage of executives do not understand how their AI models work?
A: 65% of executives surveyed could not explain how their AI models make decisions.

Q: What are academic collusion rings?
A: Collusion rings involve groups of researchers secretly working together to write positive peer reviews for each other's papers in order to get them accepted at conferences.

Q: Where was the original ELIZA source code discovered?
A: The ELIZA source code was uncovered in archived files at MIT.

Q: How much is OpenAI investing in AI startups?
A: OpenAI has launched a $100 million fund to invest in early-stage AI startups aiming to have a positive impact.

Q: What areas is OpenAI interested in funding?
A: OpenAI is looking to fund startups using AI to transform healthcare, climate change, education, and other areas.

Q: What programming language was ELIZA written in?
A: ELIZA was written in a language called MAD-Slip.

Q: Who developed the ELIZA chatbot?
A: ELIZA was developed by Joseph Weizenbaum at MIT.

Q: How does ELIZA work?
A: ELIZA uses pattern matching and canned responses to mimic a psychotherapy session. It reflects user input back at them.