Databricks has secured a $62 billion valuation after raising a whopping $10 billion in one of the largest venture capital funding rounds in history!
The notion of entropy grew out of an attempt at perfecting machinery during the industrial revolution. A 28-year-old French military engineer named Sadi Carnot set out to calculate the ultimate efficiency of the steam-powered engine. In 1824, he published a 118-page book(opens a new tab) titled Reflections on the Motive Power of Fire, which he sold on the banks of the Seine for 3 francs. Carnot’s book was largely disregarded by the scientific community, and he died several years later of cholera. His body was burned, as were many of his papers. But some copies of his book survived, and in them lay the embers of a new science of thermodynamics — the motive power of fire.
Carnot realized that the steam engine is, at its core, a machine that exploits the tendency for heat to flow from hot objects to cold ones. He drew up the most efficient engine conceivable, instituting a bound on the fraction of heat that can be converted to work, a result now known as Carnot’s theorem. His most consequential statement comes as a caveat on the last page of the book: “We should not expect ever to utilize in practice all the motive power of combustibles.” Some energy will always be dissipated through friction, vibration, or another unwanted form of motion. Perfection is unattainable.
Continue reading “What Is Entropy? A Measure of Just How Little We Really Know” »
The notion of entropy grew out of an attempt at perfecting machinery during the industrial revolution. A 28-year-old French military engineer named Sadi Carnot set out to calculate the ultimate efficiency of the steam-powered engine. In 1824, he published a 118-page book(opens a new tab) titled Reflections on the Motive Power of Fire, which he sold on the banks of the Seine for 3 francs. Carnot’s book was largely disregarded by the scientific community, and he died several years later of cholera. His body was burned, as were many of his papers. But some copies of his book survived, and in them lay the embers of a new science of thermodynamics — the motive power of fire.
Carnot realized that the steam engine is, at its core, a machine that exploits the tendency for heat to flow from hot objects to cold ones. He drew up the most efficient engine conceivable, instituting a bound on the fraction of heat that can be converted to work, a result now known as Carnot’s theorem. His most consequential statement comes as a caveat on the last page of the book: “We should not expect ever to utilize in practice all the motive power of combustibles.” Some energy will always be dissipated through friction, vibration, or another unwanted form of motion. Perfection is unattainable.
Reading through Carnot’s book a few decades later, in 1865, the German physicist Rudolf Clausius coined a term for the proportion of energy that’s locked up in futility. He called it “entropy,” after the Greek word for transformation. He then laid out what became known as the second law of thermodynamics: “The entropy of the universe tends to a maximum.”
Continue reading “Google DeepMind Unveils Veo 2, A New AI Video Model To Rival OpenAI’s Sora” »
Unlocking The Potential Of Blood — Dr. Jackie Kunzler Ph.D. — Senior Vice President, Global R&D, Terumo Blood and Cell Technologies.
Dr. Jackie Kunzler, Ph.D. is Senior Vice President and Global Head of Research and Development (R&D), and member of the Executive Management Committee, of Terumo Blood and Cell Technologies (https://www.terumobct.com/), where she focuses on innovation and development leading the way for unlocking the potential of blood and cell collections in varied sectors, including blood banking, plasma-based therapies and cell and gene therapies.
Artificial intelligence is no longer just a buzzword; it’s a transformative force reshaping industries, from healthcare to finance to retail. However, behind every successful AI system lies an often-overlooked truth: AI is only as good as the data that powers it.
Organizations eager to adopt AI frequently focus on algorithms and technologies while neglecting the critical foundation—data. Even the most advanced AI initiatives are doomed to fail without a robust data strategy. I’ll explore why a solid data strategy is the cornerstone of successful AI implementation and provide actionable steps to craft one.
Imagine building a skyscraper without solid ground beneath it. Data plays a similar foundational role for AI. It feeds machine learning models, drives predictions and shapes insights. However, as faulty materials weaken a structure, poor-quality data can derail an AI project.
“The next wave of AI will be able to augment people to become superhuman. Solutions will be at the ready for nearly all problems facing humanity.” ~Alex Bates.
Habits2Goals presents a powerful interview with Alex Bates, a phenomenal entrepreneur, inventor and bestselling author of Augmented Mind: AI Superhumans and the Next Economic Revolution.
Today, AI agents have evolved to become more modular and sophisticated. Agents like ChatGPT can engage in conversations and assist in a wide range of workflows, including customer service and financial decision-making.
Technologies such as retrieval-augmented generation (RAG) allow AI systems to combine different data sources dynamically, making them more adaptive and helpful in real-world applications. As AI’s influence expands into industries such as finance, healthcare and cybersecurity, it is becoming clear that AI agents are critical components of modern business operations.
Despite the remarkable progress in AI, deploying these systems presents several challenges. One of the primary concerns is the risk of bias embedded in the datasets used to train AI agents. AI systems learn from historical data, which can contain patterns of discrimination that, if unchecked, lead to biased decisions, such as favoring particular groups over others in hiring or lending scenarios.
Artificial intelligence, AI, is rapidly transforming work also in the financial sector. Conducted at the University of Eastern Finland, a recent study explored how integrating AI into the work of sales teams affects the interpersonal communication competence required of sales managers. The study found that handing routine tasks over to AI improved efficiency and freed up sales managers’ time for more complex tasks. However, as the integration of AI progressed, sales managers faced new kind of communication challenges, including those related to overcoming fears and resistance to change.
“Members of sales teams needed encouragement in the use AI, and their self-direction also needed support. Sales managers’ contribution was also vital in adapting to constant digital changes and in maintaining trust within the team,” says Associate Professor Jonna Koponen of the University of Eastern Finland.
The longitudinal study is based on 35 expert interviews conducted over a five-year period in 2019–2024, as well as on secondary data gathered from one of Scandinavia’s largest financial groups. The findings show that besides traditional managerial interpersonal communication competence, consideration of ethical perspectives and adaptability were significant when integrating AI into the work of sales teams.
Updated Antidot banking trojan targets Android users via fake job offers, stealing credentials and taking remote control.
However, while Google’s achievements have been noted for advancing the field, experts say that quantum computing still has no real-world uses — yet.
“We need a ChatGPT moment for quantum,” Francesco Ricciuti, associate at venture capital firm Runa Capital, told CNBC on Tuesday, referencing OpenAI’s chatbot that has been credited with driving the boom in artificial intelligence. “This is probably not that.”
Proponents of quantum computing claim it will be able to solve problems that current computers can’t.
