Oct 26, 2017. Sponsored by Lewis, a PR firm at The District in Boston
Speakers
- Forrester VP. Mike Gualtier Editor XCO Mag/Northeastern:
- Mike Farrell Sam Whitmore Media surve
- AI prof/Tech writer at Boston U: Joelle Renstom
- Canvas Ventures partner - Paul Hsiao. First capital in Siri, multiple investments in ML
Meeting Summary
- Full people like robots are years away but narrow, pragmatic AI
- “Augmented AI” is most promising
- DATA IS NEEDED to get a good model
- So many examples of applications where there is insufficient data to train the model and a feedback look
- Speakers/crowd generally agreed that adoption will increase next 5 years and jobs will be displaced, especially low wage jobs
- Automated Intelligence most promising hear term
- Truck driving autopilot
- Robots in burning buildings
Notes by Speaker
Forrester - Mike Gulatieri
- Pure AI: sci fi; human like
- Pragmatic AI: very narrow in scope but beats technical human
- Google: develop app to beat Go cham
- Watson beat chess champion
- AI comprised 9 building blocks
- ML,
- Okay to use one ML and call it AI
- “Automated intelligence” per Forrester, low skilled workers working with a robot
- Why is the ONLY home robot the Roomba for sweeping; but no robot for folding laundry
- Lots of discussion and credit are given to algorithms, but it’s all about the data.
- There is NO magic in the algorithm
- Laundry automated with perception learning. We are NOT close. We are many, many years away for this
- Deep learning in 2012 breakthrough
- Invideo
- Deep learning uses neural networks
- It’s very difficult to test the model to know where it worked
- All these models are based on probabilities
- Decision tree can be traced, but neural networks cannot be traced
- Guardrails or circuit breakers
- Google has guard rails. When model says “I think someone died, let’s show an add for caskets” and guardrails
- NO company TRUSTS their ML solely by itself without guardrails
- CIA and others ask “can we use AI to prevent cyberattacks”. Forrester says “you need A LOT more breaches” because there is enough data to look
- Anomaly detection is typically now used instead for security detections but challenge is that there is false positives
- Alexa is providing Amazon SO MUCH data because of the there are so many people using these
- HLMI - high level machine intelligence
- Survey of 200
VC landscape Paul Hsaio
- Must include AI in slide desk to get funding these days
- commoditization
- VC focus on proprietary data since so many tools are giving away tools
- The biggest challenge is lack of engineers coming into this space. Multiple acquires
- Very few engineers actually know the AI space
- Most things have become possible in the 5 years because of CPU,
- We are VERY far from AI like person that destroys job
- Robot advisors has been around 10 years in financials
- On board of Elance, paid $2B to contractors
- Automation of trucking
- Level 1 and level 2: trucks turn on autopilot once on the highway, but off highway is then human. Like autopilot for plane
- Uber IA on panel said,
- Google puts it’s own cell phones in truck to track them. 99.9% of
- Video 4 Berkely PH students trying to figure out how program a robot to fold t-shirt but still not successful. Automation is hard
- We’ve seen several startups automated medical records
- Traction for upcoding; reading the records to figure how to charge the government MORE
- Maybe we need to start re-evaluating teaching. Some human subjects and others subjects are AI based [I think he means online courses) as part of “Augmented Intelligence” rather than Artifical
- Amazon (closed system) vs Google Home (more open)
- Rapid eco system; innovation happening a RAPID pace
- What is my bank balance (683 different ways to ask for bank balance. Machine learning
- We are at the state today for AI where AOL was 56kp dialup modem
- We are beginning of a 20-25 year run