CIA Leveraging Digital Transformation Tools in HUMINT Missions

source: executivegov.com (contributed by FAN, Steve Page)  |  Image: pixabay.com

One of the United States’ most secretive agencies is using digital transformation tools such as AI and human-machine teaming as it tries to solve the nation’s toughest national security problems.

Since the CIA established the Directorate of Digital Innovation, or DDI, in 2015, the agency has increasingly encouraged entwining digital technology into its core human intelligence, or HUMINT, mission, where intelligence is obtained from human sources. Juliane Gallina, the CIA’s deputy director for digital innovation, said every DDI mission is guided by human-machine teaming, which starts with data and is improved with AI before being put to use by CIA agents. Continue reading “CIA Leveraging Digital Transformation Tools in HUMINT Missions” →

Chinese AI gets better — and cheaper

source: axios.com (contributed by Bill Amshey)  |  image: pixabay.com

Chinese AI makers have learned to build powerful models that perform almost as well as the best ones in the U.S. — for less money and with much less demand for energy, Axios’ Scott Rosenberg and Alison Snyder report.

• V3, an open-source model developed by Chinese firm DeepSeek, performs about as well on various benchmark tests as OpenAI and Anthropic’s most advanced models.
• DeepSeek says it cost just $5.6 million to train V3 — compared to the hundreds of millions of dollars American companies have spent to build and train their models.

🤖 Between the lines: The Biden administration has done a lot to advance AI in the U.S. and keep those advancements out of the Chinese government’s hands.

• It has invested heavily in domestic manufacturing for powerful chips and new energy sources. And it has imposed tight export controls to prevent those chips from reaching China, including through third countries.
• That seems to have worked in the short term, while spurring China to compete just as aggressively to develop its own tools.

The Promise of Artificial General Intelligence is Evaporating

source: mindmatters.ai (contributed by Artemus founder, Bob Wallace)  |  image: pexels.com

Revenue from corporate adoption of AI continues to disappoint and, so far, pales in comparison to the revenue that sustained the dot-com bubble — until it didn’t

hink back to when you took a science class in high school or college. Introductory physics, for example. There was one textbook and, if you learned the material in the book, you got a high grade in the class. If you were super serious, you might read a second textbook that reinforced what was in the first book and might even have added a few new concepts. A third book wouldn’t have added much, if anything. Reading a 10^th, 20^th, or 100^th textbook would surely have been a waste of time.

Large language models (LLMs or chatbots) are like that when it comes to absorbing factual information. They don’t need to be told 10, 20, or 100 times that Abraham Lincoln was the 16^th President of the United States, Paris is the capital of France, or that the formula for Newton’s law of universal gravitation is