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Schmidt thinks that if the AI sector doesn’t create protections, politicians will have to step in.

Eric Schmidt, the former CEO of Google, has spoken out against the six-month ban on AI development that some tech celebrities and business executives demanded earlier.

“I’m not in favor of a six-month pause, because it will simply benefit China,” said Schmidt, Google’s first CEO.

Continue reading “Ex-Google CEO Eric Schmidt warns pausing ChatGPT-like AI will benefit China” »

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Continue reading “GPT-5 Rumors and Predictions — It’s about to get real silly” »

Large Language Models have rapidly gained enormous popularity by their extraordinary capabilities in Natural Language Processing and Natural Language Understanding. The recent model which has been in the headlines is the well-known ChatGPT. Developed by OpenAI, this model is famous for imitating humans for having realistic conversations and does everything from question answering and content generation to code completion, machine translation, and text summarization.

ChatGPT comes with censorship compliance and certain safety rules that don’t let it generate any harmful or offensive content. A new language model called FreedomGPT has recently been introduced, which is quite similar to ChatGPT but doesn’t have any restrictions on the data it generates. Developed by the Age of AI, which is an Austin-based AI venture capital firm, FreedomGPT answers questions free from any censorship or safety filters.

FreedomGPT has been built on Alpaca, which is an open-source model fine-tuned from the LLaMA 7B model on 52K instruction-following demonstrations released by Stanford University researchers. FreedomGPT uses the distinguishable features of Alpaca as Alpaca is comparatively more accessible and customizable compared to other AI models. ChatGPT follows OpenAI’s usage policies which restrict categories like hate, self-harm, threats, violence, sexual content, etc. Unlike ChatGPT, FreedomGPT answers questions without bias or partiality and doesn’t hesitate to answer controversial or argumentative topics.

Data has emerged as one of the world’s greatest resources, underpinning everything from video-recommendation engines and digital banking, to the burgeoning AI revolution. But in a world where data has become increasingly distributed across locations, from databases to data warehouses to data lakes and beyond, combining it all into a compatible format for use in real-time scenarios can be a mammoth undertaking.

For context, applications that don’t require instant, real-time data access can simply combine and process data in batches at fixed intervals. This so-called “batch data processing” can be useful for things like processing monthly sales data. But often, a company will need real-time access to data as it’s created, and this might be pivotal for customer support software that relies on current information about each and every sale, for example.

Elsewhere, ride-hail apps also need to process all manner of data points in order to connect a rider with a driver — this isn’t something that can wait a few days. These kinds of scenarios require what is known as “stream data processing,” where data is collected and combined for real-time access, which is far more complex to configure.

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Continue reading “AGI Unleashed: Game Theory, Byzantine Generals, and the Heuristic Imperatives” »

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Continue reading “Post-Singularity Predictions — How will our lives, corporations, and nations adapt to AI revolution?” »

The release of ChatGPT in late November 2022 lit a fire under the subdued venture capital sector, a hesitant business community, and the work of academics and regulators. While venture funding decreased by 19% from Q3’22 to Q4’22, AI funding increased 15% over the same period, according to CB Insights’ State of AI 2022 Report (annual AI funding dropped by 34% in 2022, mirroring the broader venture funding downturn). Looking specifically at generative AI startups, CB Insights found that 2022 was a record year, with equity funding topping $2.6 billion across 110 deals.

Everywhere you turn, you encounter generative AI.

A new report has found that 186 banks in the US are at risk of failure due to rising interest rates and a high proportion of uninsured deposits. The research, posted on the Social Science Research Network titled ‘Monetary Tightening and US Bank Fragility in 2023: Mark-to-Market Losses and Uninsured Depositor Runs?’ estimated the market value loss of individual banks’ assets during the Federal Reserve’s rate-increasing campaign. Assets such as Treasury notes and mortgage loans can decrease in value when new bonds have higher rates.

The study also examined the proportion of banks’ funding that comes from uninsured depositors with accounts worth over $250,000.

If half of the uninsured depositors quickly withdrew their funds from these 186 banks, even insured depositors may face impairments as the banks would not have enough assets to make all depositors whole. This could potentially force the FDIC to step in, according to the paper.

Researchers from the University of Geneva (UNIGE), the Geneva University Hospitals (HUG), and the National University of Singapore (NUS) have developed a novel method for evaluating the interpretability of artificial intelligence (AI) technologies, opening the door to greater transparency and trust in AI-driven diagnostic and predictive tools. The innovative approach sheds light on the opaque workings of so-called “black box” AI algorithms, helping users understand what influences the results produced by AI and whether the results can be trusted.

This is especially important in situations that have significant impacts on the health and lives of people, such as using AI in medical applications. The research carries particular relevance in the context of the forthcoming European Union Artificial Intelligence Act which aims to regulate the development and use of AI within the EU. The findings have recently been published in the journal Nature Machine Intelligence.

Time series data—representing the evolution of information over time—is everywhere: for example in medicine, when recording heart activity with an electrocardiogram (ECG); in the study of earthquakes; tracking weather patterns; or in economics to monitor financial markets. This data can be modeled by AI technologies to build diagnostic or predictive tools.