Uncategorized

Richmond’s thriving tech community came together in force for the Richmond Technology Council’s rvatech/tech’s annual Women in Tech event. BIML’s Katie McMahon delivered the opening keynote address to a packed audience at the Dewey Gottwald Center. This year’s event saw record attendance, drawing engineers, data scientists, cybersecurity specialists, CIOs, CTOs, entrepreneurs, product leaders, members of the state administration, and representatives from the Governor’s AI Task Force.

In her keynote, McMahon broadly addressed the topic of “AI Overwhelm,” and delved into BIML’s architectural risk analysis of large language models (LLMs). She left the audience more aware of the risks associated with LLMs, while also encouraging a thoughtful and mindful approach to building products and services.

McMahon also spoke about “GenV” – Generation Voice – a term she coined in 2015 to describe those born from 2010 onward, the first digital native generation to grow up accustomed to speaking with computers. With the meteoric rise of ChatGPT and other increasingly sophisticated LLM chatbots, the implications for the user experience are far-reaching, as users increasingly leverage voice interactions and natural conversational ‘dialogue’ with computing power.

The audience was exceptional, fully attentive, engaged, and receptive to the keynote. One of the biggest compliments came from two data scientists who were scheduled to present a technical workshop later in the day, titled “Clearing the Fog: Diagnosing Hallucinations in your LLMs.” After the keynote, they approached McMahon and enthusiastically announced, “We loved your talk! And we are now going to start calling it ‘Wrongness’!”

This year’s Women in Tech event at RVA Tech once again demonstrated the depth and breadth of talent within Richmond’s thriving tech ecosystem. The keynote address that Katie presented provided thought-provoking insights into the challenges and opportunities presented by the rapid advancements in artificial intelligence.

Note: Katie would like to expressly thank rva/tech and their wonderful planning committee, including Emily Mercer (pictured in photo with Katie), Chris Burroughs and Jazmyn Ward, for producing a wonderful event and graciously hosting her.

As independent scholars, we have a huge amount of respect for professors and students of Computer Science at small colleges in the United States. We were proud to participate as the dinner speaker at the CCSC Eastern Conference this year.

Our payment was a cool T-shirt and some intellectual stimulation. (Now you know why McGraw never takes selfies.)

One time student of mine at Earlham College, one time employee of mine at Cigital, and now the infamous daveho (author of Find Bugs).

Sometimes it pays to stop and think, especially if you can surround yourself with some exceptional grad students. On the way to Rose-Hulman, BIML made a pit stop in Bloomington for a dinner focused on two papers: Vaswani’s 2017 Attention is All You Need (defining the transformer architecture) also see https://berryvilleiml.com/bibliography/ and Dennis “the antecedents of transformer models” (which will appear in Current Directions in Psychological Science soon.

The idea was to explore and critique the architectural decisions underlying the Transformer architecture. Bottom line? Most of them were made for efficiency reasons. There is lots of room for better cognitively-inspired ML. Maybe efficiency is NOT all you need.

We did this all over delicious Korean food at Hoosier Seoulmate.

Special thanks to Rob Goldstone who provided the Dennis manuscript and grounded the cognitive psychology thread and to Eli McGraw who conjured up the dinner from thin air.

The Lake Monroe home away from home.

Dr. McGraw gave a talk Wednesday 10/16/24 at Rose-Hulman in Terre Haute, Indiana. This version of the talk is aimed at Computer Science students. There were some very good questions.

Here is a video of the Dublin panel recorded October 3rd 2024. This was quite an excellent event. Have a watch.

BIML co-founder Gary McGraw joins an esteemed panel of experts to discuss Machine Learning Security in Dublin Thursday October 3rd. Participation requires registration. Please join us if you are in the area.

On the back of our LAWFARE piece on data feudalism, McGraw did a video podcast with Decipher and his old friend Dennis Fisher. Have a look.

Dan Geer came across this marketing thingy and sent it over. It serves to remind us that when it comes to ML, it’s all about the data.

Take a look at this LAWFARE article we wrote with Dan about data feudalism.

Welcome to the era of data feudalism. Large language model (LLM) foundation models require huge oceans of data for training—the more data trained upon, the better the result. But while the massive data collections began as a straightforward harvesting of public observables, those collections are now being sectioned off. To describe this situation, consider a land analogy: The first settlers coming into what was a common wilderness are stringing that wilderness with barbed wire. If and when entire enormous parts of the observable internet (say, Google search data, Twitter/X postings, or GitHub code piles) are cordoned off, it is not clear what hegemony will accrue to those first movers; they are little different from squatters trusting their “open and notorious occupation” will lead to adverse possession. Meanwhile, originators of large data sets (for example, the New York Times) have come to realize that their data are valuable in a new way and are demanding compensation even after those data have become part of somebody else’s LLM foundation model. Who can gain access control for the internet’s publicly reachable data pool, and why? Lock-in for early LLM foundation model movers is a very real risk.

REGISTER HERE.

BIML coined the term data feudalism in our LLM Risks document (which you should read). Today, after a lengthy editing cycle, LAWFARE published an article co-authored by McGraw, Dan Geer, and Harold Figueroa. Have a read, and pass it on.

https://www.lawfaremedia.org/article/why-the-data-ocean-is-being-sectioned-off