The Mediterranean Diet and Nutritional Adequacy: A Review.

Castro-Quezada, I., Román-Viñas, B., & Serra-Majem, L. (2014). The Mediterranean Diet and Nutritional Adequacy: A Review. Nutrients.

Interesting … the claims

The Mediterranean dietary pattern, through a healthy profile of fat intake, low proportion of carbohydrate, low glycemic index, high content of dietary fiber, antioxidant compounds, and anti-inflammatory effects, reduces the risk of certain pathologies, such as cancer or Cardiovascular Disease (CVD).

The inclusion of foods typical of the Mediterranean diet and greater adherence to this healthy pattern was related to a better nutrient profile, both in children and adults, with a lower prevalence of individuals showing inadequate intakes of micronutrients.

We’ll be reviewing this and related studies in the overall evaluation …

later …

 

Towards an Open Research Knowledge Graph – One must chuckle

One must chuckle … I was looking at Towards an Open Research Knowledge Graph by
Sören Auer & Sanjeet Mann

It’s behind a paywall …

Can we please not use the phrase Open Anything unless its actually open and available without a paywall 🙂

 

 

 

Tools Tools Tools

adding a page of tools to facilitate deep research

Stanley & Lehman – Why Greatness Cannot Be Planned

Fascinating insights by Computer Science / Artificial Intelligence profs …

https://amzn.to/2DlhLnX

some have summarized their insights by writing: “only by doing activities that fulfill our curiosity without any pre-defined objectives, true creativity can be unleashed. They call this the ‘Myth of the Objective’: Objectives are well and good when they are sufficiently modest … In fact, objectives actually become obstacles towards more exciting achievements, like those involving discovery, creativity, invention, or innovation—or even achieving true happiness… the truest path to “blue sky” discovery or to fulfill boundless ambition, is to have no objective at all.”

some of Stanley’s and Lehmans insights:

 

• “The flash of insight is seeing the bridge to the next stepping stone by building from the old ones. ”

 

• “[Picbreeder] is just one example of a fascinating class of phenomena that we might call non-objective search processes, or perhaps stepping stone collectors. The prolific creativity of these kinds of processes is difficult to overstate”

 

• “ measuring success against the objective is likely to lead you on the wrong path in all sorts of situations”

 

• “You can’t evolve intelligence in a Petri dish based on measuring intelligence. You can’t build a computer simply through determination and intellect—you need the stepping stones. ”

 

• “ambitious objectives are the interesting ones, and the idea that the best way to achieve them is by ignoring them flies in the face of common intuition and conventional wisdom. More deeply it suggests that something is wrong at the heart of search. ”

 

 

I find their books inspiring and insightful. Reframing questions and providing different lines of attack on AI and Search Optimization to Ambitious Goals …

 

 

 

 

Geoffrey Hinton: The Foundations of Deep Learning

Excellent initial tutorial by DL Olympianian – Geoffrey Hinton: The Foundations of Deep Learning on you tube …

right here

catch up  here

Deep Learning AI Playbook: Strategy for Disruptive Artificial Intelligence

Today … looking at Carlos Perez’s Deep Learning AI Playbook: Strategy for Disruptive Artificial Intelligence 

The claim: “This book is an opinionated take on the developments of Deep Learning AI” – a reviewers offers that it’s probably good to start out exploring the  Intuition Machine blog on Medium.    Appears oriented to the ‘first-entry into AI ‘ folks who want to get a sense of what’s going on. Really Deep AI /Deep Learning / Deep whatever is another matter.

Ford’s Architects of Artificial Intelligence

 

To place Artificial Intelligence in appropriate context is a complex and intricate challenge.  Marty Ford, a master explainer presents interviews with some of the principal architects of AI.  The Architects in this case are Yoshua Bengio, Stuart Russell, Geoffrey Hinton, Nick Bostrom, Yann LeCun, Fei-Fei Li, Demis Hassabis, Andrew Ng, Rana El Kaliouby, Ray Kurzweil, Daniela Rus, James Manyika, Gary Marcus, Barbara Grosz, Judea Pearl, Jeffrey Dean, Daphne Koller, David, Ferrucci, Rodney Brooks, Cynthia Breazeal, Joshua Tenenbaum, Oren Etzioni, and Bryan Johnson.

What a great list … Dan Ferrucci is of course known from his amazing work with IBM WATSON, and the first ever amazing win of a machine over the best of the best at Jeopardy!  Dennis Hassabis , of Google/Alphabet’s Deep Mind, brought us AlphaGo, AlphaGoZero, and now AlphaGo that exceeds the best of the best in Chess, GO and Shogi (all with the same MCTS algorithm). NYU/FAIR/FaceBook’s Yan LeCun did some serious stuff with Mastering and claiming ‘the prize’ over ImageNet Challenge. Rodney Brooks with iRobot, … each one of the Architects is truly a master architect.  We’ll explore their contributions and significance later … their thoughts are really worth checking out.  I am looking at all kinds of things right now … and there’s just so much.  Maybe I need a nice Intelligent Machine Assistant to help me pull this al together.  🙂

Ford, M. (2018), Architects of Intelligence, 

AI, The Real History: McCorduck’s Machines who Think

“The occupational activities of children are learning, thinking, playing and the like. Yet we tell them nothing about those things.”  per AI Pioneer Seymour Papert –  In Pam McCorduck’s Machines who Think, (an outstanding book; Pam is a great author, turns out she’s the wife of Joe Traub who was Computer Science Dept Chair at Carnegie Mellon University & Columbia University … and had amazing insight into the real story 🙂 – not found elsewhere ) https://amzn.to/2FwGmIu 

EXCELLENT EXCELLENT BOOK … It’s really packed with amazing insights and details hidden from the public view …

I didn’t realize Papert’s connection with Piaget and his deep understanding and interest in how children learn.  Of course Papert and Minsky’s Perceptrons were widely known [ and got a refresh boost . The Perceptron. ideas… which, in prehistoric times, with Marvin Minsky, helped pave the way to the AI we know today. — that’s where the real action was and maybe still is …  check the reboot. over at  https://amzn.to/2TNjok7

 

Coming up soon – in my book: Regarding Natural, Artificial & Other (?) Intelligences.

One of the main categories of discussion in this book is that of worthwhile tasks for AI. I will devote some time to stating some of the recognized questions, problems, and tasks. I will also mention some notable AI accomplishments and highlight a few of the recognized scholarly achievements. Another topic for discussion is the classification of Intelligences. What is Natural Intelligence? What is Artificial General Intelligence? What is Superintelligence? What about human measures such as IQ? G? What does the AlphaZero algorithm beating the best human players in Chess, Go and Shogi mean? Can the Paperclip Apocalypse really happen?

All these and more … coming soon …

Deep Learning on My Mind

OK, so I started perusing Terry  Sejnowski’s   recent book,  The Deep Learning Revolution.  It’s dedicated to Bo and Sol, Theresa, and Joseph and is In memory of Solomon Golomb.  Nice!

• It’s a great book. In the short time I spend with it,  I learned quite a lot. I decided to see what’s most important to Terry looking at the topics he spends most of his time on.  Here’s what pops out first …neural networks and deep learning . [To be expected], then the items getting most discussion are:
• the brain
• machine learning
• learning algorithm
• artificial intelligence
• the world
• visual cortex
• the network
• boltzmann machine
• the cortex
• Geoffrey Hinton [looks like Geoff is really getting attention and kudos from everyone!!]
• network models
• the future
• learning
• self driving car
• learning networks
• cost function
• deep learning networks
• hopfield net
• primary visual cortex
• the visual cortex
• independent component analysis
• real world
• brains
• the internet
• the perceptron
• facial expressions
• reinforcement learning
• Francis Crick
• hidden units
• the retina
• information processing systems
• neural information processing
• neural information processing systems
• td gammon
• the boltzmann machine
• computer vision
• driving cars
• simple cells
• the hopfield net
• cerebral cortex
• David Hubel

Somewhere further down the list I came across Soumith Chintala over at FaceBook AI / Courant Institute.  His was a new name for me. Looks like he’s a PyTorch maven, super-coder. Nice! his Wasserstein Generative Adversarial Network (GAN) paper seems pretty nice.  Apparently FAIR has advanced the ball a lot with Generative Adversarial Networks. I need to  be paying much more attention.  Also noted a new name to follow, Cade Metz  who writes about  technology for The New York Times/

All this from my first glance at The Deep Learning Revolution.  

read it … I will get deeper into the deep learning as well.

Happy Holidays …