Lifeboat Foundation

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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GitHub: https://github.com/daveshap.
Cognitive AI Lab Discord: https://discord.gg/yqaBG5rh4j.

Artificial Sentience Reddit: https://www.reddit.com/r/ArtificialSentience/
Heuristic Imperatives Reddit: https://www.reddit.com/r/HeuristicImperatives/

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

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GitHub: https://github.com/daveshap.
Discord: https://discord.gg/yqaBG5rh4j.

Reddit: https://www.reddit.com/r/ArtificialSentience/
Reddit: https://www.reddit.com/r/HeuristicImperatives/

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.

During a tense opening weekend at SXSW, following the sudden collapse of Silicon Valley Bank which banked nearly half of US venture-backed startups, billionaire investor Mark Cuban sat down with me to discuss options for entrepreneurs trying to secure funds in the midst of unprecedented economic chaos.

“I would encourage people to do their homework,” he said. “This is a learning experience. It’s been a learning experience for me.”

With credit tightening and banks failing, many startups are having a hard time accessing the private equity markets. But fortunately, capital is available from a variety of sources without having to give up equity. Interviews with Mark Cuban and others highlight funds awarding big bucks.

Continue reading “Mark Cuban, SBA And Google On How AI Startups Can Get $2 Million In Funding Amid The Bank Crisis” »

Tang Yu, the AI CEO of the gaming company NetDragon Websoft, was assigned typical duties of the company that included reviewing high-level analytics, making leadership decisions, assessing risks, and fostering an efficient workplace. Tang Yu was the first CEO of a company that worked 24×7, without receiving compensation. The company said while appointing the AI chatbot as CEO that it will play a crucial role in the development of talents and ensuring a far and efficient workplace for all employees.

“Tang Yu’s appointment highlights the Company’s “AI + management” strategy and represents a major milestone of the Company towards being a “Metaverse organization”. Tang Yu will streamline process flow, enhance the quality of work tasks, and improve speed of execution. Tang Yu will also serve as a real-time data hub and analytical tool to support rational decision-making in daily operations, as well as to enable a more effective risk management system,” NetDragon Websoft said in a blog post.

Interestingly, the AI bot helped the company in generating revenue. As per reports, the company outperformed the Hang Seng Index, which tracks the biggest companies listed in Hong Kong.

Black box optimization methods are used in every domain, from Artificial Intelligence and Machine Learning to engineering and finance. These methods are used to optimize functions when an algebraic model is absent. Black box optimization looks into the design and analysis of algorithms for those problem statements where the structure of the objective function or the limitations defining the set is not known or explainable. Given a set of input parameters, black box optimization methods are designed to evaluate the optimal value of a function. This is done by iteratively assessing the function at multiple points in the input space so as to find the point that generates the optimal output.

Though gradient descent is the most used optimization approach for deep learning models, it is unsuitable for every problem. In cases where gradients cannot be calculated directly or where an objective function’s accurate analytical form is unknown, other approaches like Evolution Strategies (ES) are used. Evolution strategies come from evolutionary algorithms, which refer to a division of population-based optimization algorithms inspired by natural selection. Basically, Evolution Strategies (ES) is a type of Black Box Optimization method that operates by refining a sampling distribution based on the fitness of candidates and updating rules based on equations.

In a new AI paper, researchers from Deepmind, have introduced and developed a new way to use machine learning to learn the update rules from data, called meta-black-box optimization (MetaBBO), to make ES more flexible, adaptable, and scalable. MetaBBO works by meta-learning a neural network parametrization of a BBO update rule. The researchers have used MetaBBO to discover a new type of ES called learned evolution strategy (LES). The learned evolution strategy LES is a type of Set Transformer that updates its solutions based on the fitness of candidates and not depending upon the ordering of candidate solutions within the Black box evaluations. After meta-training, the LES can learn to choose the best-performing solution or update solutions based on a moving average.