Anthropic Raises $124M, Execs Can't Explain AI Decisions, and More
Table of Contents
- Anthropic raises $124 million for steerable AI
- 65% of execs can't explain how their AI models make decisions
- DeepMind releases AndroidEnv the Android learning environment
- Collusion rings threaten the integrity of computer science research
- Joseph Weizenbaum's original source code for the Eliza program discovered
- Open AI opens $100 million fund to help AI companies have a positive impact
- Introduction to ML news
- Anthropic: A New AI Research Company
- Lack of Explainability in AI Models
- AndroidEnv: Reinforcement Learning on Android Apps
- The Threat of Collusion Rings in Computer Science Research
- Rediscovering Joseph Weizenbaum's Source Code for Eliza
Anthropic raises $124 million for steerable AI
Anthropic TechCrunch reports that Anthropic, a new AI research company founded by Dario Amodie of OpenAI and his sister Daniela Amodie, has raised $124 million in a Series A funding round. Led by Jaan Tallinn, the co-founder of Skype, and other prominent investors such as Eric Schmidt and Dustin Moskovitz, Anthropic focuses on developing reliable, interpretable, and steerable AI systems. The company's goal is to make fundamental research advances that will enable the creation of more capable and reliable AI systems. They emphasize the importance of deploying these systems in a way that benefits everyone and adhere to research principles centered around AI as a systematic science, safety and scaling, and the development of tools and measurements to assess progress towards general AI.
65% of execs can't explain how their AI models make decisions
According to a survey conducted by FICO and Corinium, 65% of C-level analytic and data executives admit that they cannot explain how their AI models make decisions or predictions. While some may interpret this as a cause for concern, it is essential to consider the Context. These executives are not expected to possess technical knowledge of how complex AI models operate. Similar to their understanding of Excel spreadsheets, they rely on the expertise of AI professionals in their organizations. However, the survey underscores the importance of transparency and interpretability in AI systems to build trust and address potential bias or discrimination.
DeepMind releases AndroidEnv: The Android learning environment
DeepMind has released AndroidEnv, an innovative learning environment for android apps. Building upon the Android emulator, AndroidEnv provides a unified interface and tasks that allow for the application of reinforcement learning techniques. This platform opens up new possibilities for multitask learning, Perception, and interaction with real-world apps. The availability of AndroidEnv on GitHub, along with its accompanying example tasks, enables practitioners interested in reinforcement learning and its applications in robotics to experiment and develop their own models and algorithms.
Collusion rings threaten the integrity of computer science research
In an article published in the Communications of the ACM, Michael Littman warns about the existence of collusion rings that pose a significant threat to the integrity of computer science research. Collusion rings involve individuals secretly working together to manipulate the Peer review process and favor the acceptance of their papers. These colluders engage in biased reviewing practices and exert influence over other reviewers and area chairs. While Littman does not disclose the specific conference or colluders involved, he aims to Raise awareness about this issue and the challenges it presents to the scientific community.
Joseph Weizenbaum's original source code for the Eliza program discovered
The original source code for the Eliza program, developed by Joseph Weizenbaum, has been discovered in the archives of MIT. Eliza was an early AI program that sparked interest in human-computer interaction but struggled to meet expectations. The source code, implemented in a language called math’s lip, reveals the pattern matching algorithm and pre-canned responses used by Eliza to simulate a Rogerian therapist. While Eliza's limitations became apparent, it remains a significant milestone in the history of AI. The availability of the source code provides an opportunity for researchers interested in early AI systems to explore and study its mechanics.
Open AI opens $100 million fund to help AI companies have a positive impact
Open AI has announced the establishment of a $100 million fund intended to support early-stage AI companies that have a profound positive impact. The fund aims to invest in ventures focused on areas such as healthcare, climate change, and education, where AI technology can bring transformative change. Open AI's goal is to ensure that the benefits of AI are distributed broadly and evenly. While they plan to invest in a limited number of startups, the application process is open to all eligible companies that Align with their mission.
Introduction to ML news
Welcome to ML news—a regular update on the latest happenings in the world of machine learning. In this article, we'll take You through some of the notable stories from the past week or so in the ML domain. From significant funding rounds and new research initiatives to discoveries in AI history and the challenges faced by the industry, we've got you covered. So without further ado, let's dive into the fascinating world of ML!
Anthropic: A New AI Research Company
Anthropic, a newly established AI research company, is making waves in the industry. Founded by Dario Amodie, formerly of OpenAI, and his sister Daniela Amodie, Anthropic focuses on the development of reliable, interpretable, and steerable AI systems. The company recently secured $124 million in a Series A funding round led by Jaan Tallinn, the co-founder of Skype, and other notable investors. Anthropic's mission is to make fundamental research advances that will pave the way for the creation of more capable and reliable AI systems. Their research principles revolve around AI as a systematic science, prioritizing safety and scaling, and fostering tools and measurements to track progress towards general AI that benefits all.
Lack of Explainability in AI Models
A survey conducted by FICO and Corinium reveals an interesting trend in the AI industry. Out of 100 C-level analytic and data executives surveyed, 65% expressed their inability to explain how their AI models make decisions or predictions. While this may raise concerns about the lack of transparency in AI systems, it is crucial to consider the context. Executives at this level do not possess the technical expertise expected of AI researchers or developers. Instead, they rely on the expertise of their data science teams to design and implement AI models. However, the survey highlights the need for increased transparency and interpretability in AI systems to build trust and ensure accountability.
AndroidEnv: Reinforcement Learning on Android Apps
DeepMind has unveiled an exciting development in the field of reinforcement learning: AndroidEnv. This learning environment builds upon the capabilities of the Android emulator to provide a unified interface and tasks for reinforcement learning experiments. AndroidEnv opens up new avenues for multitask learning, perception, and interaction with real-world Android apps. With the availability of the environment and a range of example tasks on GitHub, researchers and practitioners interested in reinforcement learning and its potential applications in robotics can explore and build upon this platform. This development marks an important step toward bridging the gap between simulated environments and real-world applications.
The Threat of Collusion Rings in Computer Science Research
The integrity of computer science research is under threat from collusion rings, warns Michael Littman in an article published in the Communications of the ACM. Collusion rings consist of a group of individuals who secretly collaborate to manipulate the peer review process to their AdVantage. These colluders engage in biased reviewing practices, bid on each other's papers, and lobby other reviewers and area chairs to ensure their papers are accepted. The existence of collusion rings casts doubt on the integrity and fairness of the scientific community. While this article does not disclose specific instances or names, it serves as a reminder of the importance of transparency and ethical conduct in research.
Rediscovering Joseph Weizenbaum's Source Code for Eliza
In a fascinating discovery, the original source code for the Eliza program developed by Joseph Weizenbaum has been found in the archives of MIT. Eliza, an early AI program, gained popularity for simulating a Rogerian therapist by utilizing pattern matching techniques. The newly discovered source code provides insights into Eliza's conversational principles and the implementation of the program. Although Eliza's capabilities were limited, it played a significant role in shaping the early field of AI. Researchers and enthusiasts now have the opportunity to examine and study the code, gaining a deeper understanding of the program's workings and its impact on the field of AI.
