Lifeboat Foundation

DARPA seeks to revolutionize the practice of anti-money laundering through its A3ML program. A3ML aims to develop algorithms to sift through financial transactions graphs for suspicious patterns, learn new patterns to anticipate future activities, and develop techniques to represent patterns of illicit financial behavior in a concise, machine-readable format that is also easily understood by human analysts. The program’s success hinges on algorithms’ ability to learn a precise representation of how bad actors move money around the world without sharing sensitive data.

DARPA wants to eliminate global money laundering by replacing the current manual, reactive, and expensive analytic practices with agile, algorithmic methods.

Money laundering directly harms American citizens and global interests. Half of North Korea’s nuclear program is funded by laundered funds, according to statements by the White House¹, while a federal indictment alleges that money launderers tied to Chinese underground banking are a primary source of financial services for Mexico’s Sinaloa cartel ².

Despite recent anti-money laundering efforts, the United States (U.S.) still faces challenges in countering money laundering effectively for several reasons. According to Congressional research, money laundering schemes often evade detection and disruption, as anti-money laundering (AML) efforts today rely on manual analysis of large amounts of data and are limited by finite resources and human cognitive processing speed³.

This behavior highlights a critical issue: even systems designed for seemingly harmless tasks can produce unforeseen outcomes when granted enough autonomy.

The challenges posed by AI today are reminiscent of automated trading systems in financial markets. Algorithms designed to optimize trades have triggered flash crashes —sudden, extreme market volatility occurring within seconds, too fast for human intervention to correct.

Similarly, modern AI systems are built to optimize tasks at extraordinary speeds. Without robust controls, their growing complexity and autonomy could unleash consequences no one anticipated—just as automated trading once disrupted financial markets.

Generative AI requires a new set of KPIs to measure success. These KPIs help track model accuracy, operational efficiency, user engagement, and financial impact, ensuring that AI investments deliver tangible ROI.

A data breach earlier this year at SRP Federal Credit Union has left nearly a quarter-million people exposed to possible identity theft and account fraud.

The ransomware group Nitrogen has claimed responsibility for extracting 650 gigabytes of sensitive customer data, according to reports filed recently with the state attorney general’s offices in Texas and Maine. The breach has been publicly reported throughout December by cybersecurity analysts, financial technology companies and national news media.

Screen captures of what seemed to be raw customer data from SRP were posted on social media through bogus accounts as early as Dec. 5.

Time is vital to the functioning of our everyday lives: from the watches on our wrists to the GPS systems in our phones. Communication systems, power grids, and financial transactions all rely on precision timing. Seconds are the vital units of measurement in timekeeping.

Surprisingly, there is still debate over the definition of the second. But recent advances in the world’s most accurate forms of timekeeping may have just changed the game.

Accurate timekeeping has always been part of humankind’s social evolution. At the Neolithic monument of Newgrange in Ireland, a special opening above an entrance allows sunlight to illuminate the passage and chamber on the shortest days of the year, around December 21st, the winter solstice.

Venture funding into Europe is heading for a flat year, but this may obfuscate the fact that European AI startups are thriving.

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” »