Chinese enterprises increased patent filings for artificial intelligence products rapidly in the past couple of years. The companies holding the most active AI and machine learning patent families are now tech giant Tencent and search engine provider Baidu, ahead of U.S. firm IBM, South Korea's Samsung, Chinese insurance provider Ping An and former AI patent leader Microsoft. The latter company has been seeing one of its major AI investments come to fruition recently, as conversational AI bot ChatGPT by Microsoft partner OpenAI has been making waves. Microsoft swiftly announced another round of funding for OpenAI, rumored to be to the tune of $10 billion. As this chart based on the LexisNexis PatentSight directory shows, Tencent and Baidu became the largest patent owners in machine learning and AI in 2021, each holding more than 9,000 active patent families.

Microsoft is planning to release a new and improved version of their Bing search engine, in collaboration with OpenAI. The new Bing is expected to be much more popular and efficient than the previous version, and could even give Google Search a run for its money. Microsoft has invested a lot of money into the development of this new Bing, and it is hoped that the project will be beneficial for both parties involved.

Generative AI is taking the industry by storm, and startups are scrambling to get a piece of the pie. But where is the real value in this market? We spoke with industry insiders and discovered some surprising insights. From the infrastructure giants reaping the most profits to the app companies struggling to retain users and differentiate themselves. And what about the model providers who started it all?

Schmidt said there are three significant dangers that could result from generative AI, the first being the creation of killer biological viruses. "Viruses turn out to be relatively simple to construct," he said. "An AI system using generative design techniques, plus a database of how biology actually works, and a machine that makes the viruses, which do exist, can start building terrible viruses." Second, bad actors can use generative AI tools to create and target misinformation, which Schmidt said could lead to violence. Lastly, Schmidt said generative AI can be dangerous when its decision-making is faster than humans, particularly in critical situations.

We demonstrate a proof-of-concept of a large language model conducting corporate lobbying related activities. An autoregressive large language model (OpenAI's text-davinci-003) determines if proposed U.S. Congressional bills are relevant to specific public companies and provides explanations and confidence levels. For the bills the model deems as relevant, the model drafts a letter to the sponsor of the bill in an attempt to persuade the congressperson to make changes to the proposed legislation. We use hundreds of novel ground-truth labels of the relevance of a bill to a company to benchmark the performance of the model. It outperforms the baseline of predicting the most common outcome of irrelevance. We also benchmark the performance of the previous OpenAI GPT-3 model (text-davinci-002), which was the state-of-the-art model on many academic natural language tasks until text-davinci-003 was recently released. The performance of text-davinci-002 is worse than the simple baseline. Longer-term, if AI begins to influence law in a manner that is not a direct extension of human intentions, this threatens the critical role that law as information could play in aligning AI with humans. Initially, AI is being used to simply augment human lobbyists for a small portion of their daily tasks. However, firms have an incentive to use less and less human oversight over automated assessments of policy ideas and the written communication to regulatory agencies and Congressional staffers. The core question raised is where to draw the line between human-driven and AI-driven policy influence.

One might assume that when your boss finally comes to tell you that the robots are here to do your job, he won't also point out with enthusiasm that they're going to do it 10 times better than you did. Alas, this was not the case at BuzzFeed. Yesterday, at a virtual all-hands meeting, BuzzFeed CEO Jonah Peretti had some news to discuss about the automated future of media. The brand, known for massively viral stories aggregated from social media and being the most notable progenitor of what some might call clickbait, would begin publishing content generated by artificial-intelligence programs. In other words: Robots would help make BuzzFeed posts. "When you see this work in action it is pretty amazing," Peretti had promised employees in a memo earlier in the day.

Alphabet Inc.'s Google, the pioneer of some of the technology that paved the way for a recent string of eye-catching developments in artificial intelligence, is now trying to play catch-up. In recent months, Google's competitors have publicly released AI-based programs that can generate images and text passages from simple prompts, capabilities that the tech giant has tested internally for years.

National Institute of Standards and Technology (NIST), a US-based federal agency responsible for building technology standards, has released artificial intelligence risk management framework (AI RMF 1.0), which can be used by companies to build and use AI systems in an ethical and risk-free manner. Developed in collaboration with private and public sector organisations, AI RMF framework is voluntary, which means it's usage is not binding on any company. However, NIST director Laurie E. Locascio believes that it can help large and small organisations across sectors manage their AI related risks more effectively. The framework is part of NIST's larger goal of "cultivating trust" in AI technologies within all communities, added Locascio. "It should accelerate AI innovation and growth while advancing -- rather than restricting or damaging -- civil rights, civil liberties and equity for all," Don Graves, Deputy Commerce Secretary, said in a statement.

As the name suggests, they automatically encode and decode the data. The encoder encodes the source data into a compressed file and the decoder decodes it to the original format. Auto-encoders are present in artificial neural networks, which encode the data. If these autoencoders are trained properly, the encoder at each iteration would compare the data with the source data, and tries to match the perfect output.

By 2025, Gartner expects more than 30% of new drugs and materials to be systematically discovered using generative AI techniques.