Of course, you should listen to the talk – let me blog about a couple of points that are of interest to me:
Algorithms that get better with Data
Peter had two good points:
Algorithms behave differently as they churn thru more data. For example in the figure, the Blue algorithm was better with a million training dataset. If one had stopped at that scale, one would be tempted to optimize that algorithm for better performance
But as the scale increased, the purple algorithm started showing promise – in fact the blue one starts deteriorating at larger scale. The old adage “don’t do premature optimization” is true here as well.
In general, Google prefers algorithms that get better with data. Not all algorithms are like that, but Google likes to go after the ones with this type of performance characteristic.
There is no serendipity in Google Search or Google Translate
There is no serendipity in search – it is just rehashing. It is good for finding things, but not at all useful for understanding, interpolation & ultimately inference. I think Intelligent Search is an oxymoron ;o)
Same with Google Translate. Google Translate takes all it’s cue from the web – it wouldn’t help us communicate with either the non-human inhabitants of this planet or any life form from other planets/milky ways.
In that sense, I am a little disappointed with Google’s Translation Engines. OTOH, I have only a minuscule view of the work at Google.
Data Science & the profession of a Data Scientist is being debated, rationalized, defined and refactored … I think the domain & the profession is maturing and our understanding of the Mythical Data Scientist is getting more pragmatic.
I don’t fully agree with the diagram – it has lot less programming & little more math; math is usually built-in the ML algorithms and the implementation is embedded in math libraries developed by the optimization specialists. A Data Scientist should n’t be twiddling with the math libraries
In fact that would be Natural Intelligence ! Intelligence is intelligence – it is a way of processing information to arrive at inferences, recommendations, predictions and so forth …
May be it is that Contemporary AI is actually just NI !
Point #1 : Machines are thinking like humans rather than acting like Humans
Primitives inspired by Computational Neuroscience like DeepLearning are becoming mainstream. We are no more enamored with Expert Systems that learn the rules & replace humans. We would rather have our machines help us chug through the huge amount of data.
We would rather interact with them via Google Glass – a two-way, highly interactive medium that act as a sensor array as well as augment cognition with a digital overlay over the real world
In fact, till now, our computers were mere brutes, without the elegance and finesse of the human touch !
Now the computers are diverging from Newtonian determinism to probabilistic generative models.
Instead of using greedy algorithms, the machines are now being introduced to Genetic Algorithms & Simulated Annealing. They now realize that local minima, computed via exhaustive brute force, are not the answers for all problems.
They now have knowledge graphs and have the capability to infer based on graph traversals and associated logic
Of course, deterministic transactional systems have their important place – we don’t want a probabilistic bank balance!
Point #2 : We don’t even want our machines to be like us
The operative word is “Augmented Cognition” – our machines should help us where we are not strong and augment our capabilities. More later …
Taking a cue from the contemporary media, “Person Of Interest” is a better model than “I,Robot” or “Almost Human” – a Mr.Spock, rather than a Sonny; Logical but resorts to the improbable and the random, when the impossible has been eliminated !
Point #3 : Now we are able to separate Interface from Inference & Intelligence
NLP (Natural Language Processing) and it’s first cousin NLU(Natural Language Understanding) are not intelligence, they are interface.
In fact, the team that built IBM Watson realized that “they didn’t need a genius, … but build the world’s most impressive dilettante … battling the efficient human mind with spectacular flamboyant inefficiency”.
Taking this line of thought to it’s extreme, one can argue that Google (Search) itself is the case and point of an ostentatious and elaborate infrastructure for what it does … no intelligence whatsoever – Artificial or Natural ! It should have been based on knowledge graph rather than a referral graph. Of course, in a few years, they would have made huge progress, no doubt.
Since then, IBM Watson. itself, has made rapid progress in the areas of Knowledge Traversal & Contextual Probabilistic Inferences i.e. ingest large volume of unstructured data/knowledge & reason about it
I am not trivializing the effort and the significance of machines to understand the nuances of human interactions (speech, sarcasm, slang, irony, humor, satire et al); but we need to realize that, that is not an indication of intelligence or a measure what machines can do.
Human Interface is not Human Intelligence, same with machines. They need not look like us, walk like us, or even talk like us. They just need to augment us where we are not strong … with the right interface, of course
Gary Markus in New Yorker article “Can Super Mario Save AI” says “Human brains are remarkably inefficient in some key ways: our memories are lousy; our grasp of logic is shallow, and our capacity to do arithmetic is dismal. Our collective cognitive shortcomings are so numerous … And yet, in some ways, we continue to far outstrip the very silicon-based computers that so thoroughly kick our carbon-based behinds in arithmetic, logic, and memory …“
Well said Gary. Humans & Machines should learn from the other and complement … not mimic each other … And there is nothing Artificial about it …
I took down some notes and created couple of collages out of the presentations.
You see, their value proposition goes beyond convenience. They provide a shopping experience beyond the casual encounter in a store or browse on a web page -The ability to find things that one wouldn’t have find on one’s own – and that is priceless!
I had the Fitbit Flex for a few months. But am not that satisfied with it. Is Nike FuelBand+ any better ? Plan to find out, by wearing them both for a week or two. Am tracking a few factors, pl suggest more …
The style & form factor of Nike FuleBand +
I am sorry guys, Fitbit is ugly ;o(
Fitbit sends you e-mail when the battery is low ! Good work guys
Hours won feature of Nike+
Good concept. Basically, it wants to you to move periodically
Fitbit is a lot lighter than Nike+
Nike Fuel, while a good concept, is a black box. Couldn’t internalize it
Hours won notification of Nike+ is useless. It shows on the band’s display, but I miss it most of the time. Others notice the scrolling display !
The metrics like steps et al from both bands are so different that they can’t be compared
Fitbit is maturing. They add more devices in a short span and there is a fundamental difference between the versions. Would have been good if they have an upgrade plan
Tracking sleep on both are not that useful
Stardate 91454.39 Day 1 : Both devices fully charged
Stardate 91461.40 Day 3 : Have been wearing both devices for 3 days. Updating the comparison.
Stardate : Updated Summary above.
Comparison: (I will start filling-in in as I go along):
Aesthetics – Fashion vs. Functionality:
Of course, Fuelband + is a lot good looking.
Fitbit should definitely change it’s looks.
First cut, Fitbit looks better, may be because I am use to it.
Day 3: Am used to Nike+ dashboard as well. The Nike+ dashboard on iPhone 5S looks very functional
Nike has only one main trackable feature NikeFuel against a goal.
Fitbit has multiple features – each one with it’s own goals
Two Screens after Day 1 below
Nike Fuel Graph : My Nike Fuel goal is 2500. Haven’t yet figured out how it is calculated
Hours Won is interesting. Keeps you moving
Steps, Calories – Informational. But they are different for the devices.
Very Active Minutes – 30 min is my goal. Good metric to track
Distance, Steps, Calories – Normal metrics
Both devices have badges, trophies, buddy system (friends, groups). I haven’t explored them yet.
May be I will buddy with a turtle and feel good ;o)
Fitbit does nothing except log it
Nike has an alert mechanism. Let us see if it works
I will jot down counters from both devices. Let us see how they stack up
Here are the screen shots for 2 days. I took the shots at the same time.
The devices do not agree at all. I think Fitbit is far off on the plus side while Nike might be more closer.
This was my first concern with Fitbit and I had contacted their support. Didn’t get a satisfactory response. The support folks are very good, but I think this i a technical fault.
Fitbit’s sleep tracking is a little awkward. You need to log time went to bed & time woke up. As far as I can tell, if you miss one day it is gone
Nike has the session feature, I haven’t yet tested it
Battery Life and ease of charging:
Fitbit sends an e-mail when the battery is low. Very cute and useful.
Let us see what Nike Fuelband+ has in store for us
Conclusions after 3 days
I think I will go with Nike+
Nike is more mature in many ways
Nike Fuel is a better motivation than FitBit
Overall Nike has a better form factor & a better app
Fitbit has better counters & goals for each metric. But they are not cohesive
Tracking sleep, while kludgy, is better with Fitbit ! (I never thought I will say this ;o))
Fitbit has the premium subscription ($50/yr) that gives more analytics.
But am not sure it is worth the price. I think it is an overkill.
And Nike has the analytics feature in the base product. Of course, Nike might add a paid feature set
Fitbit Flex lacks the display
I bought the Fitbit Flex and they have the Fitbit Force. Came out within 3-4 months after they introduced the Flex. I think they should have provided an upgrade path
I think Fitbit need at least couple more product revs to add a better display
Not so fast !
11/11/13 : Nike+ FuelBand crashed ! I get an page full of error when I connect it – even the Nike web site is crashing ! Looks like the Fuelband crashes the Nikeplus site !
I couldn’t fond a way to report this to Nike. Finally send them a mail via their site & twitter
I also contacted the Fitbit guys to see if I can swap out the flex for their force. They released Flex too soon.
Let us see how the response is from both the companies …
11/11/13 : Night
Heard from both. So support is good from Nike and Fitbit.
Nike’s twitter support came back. Mail via web site is stuck somewhere
Fitbit (via e-mail) politely refused to swap my Flex for Force.
I understand – I bought the device 5 months ago.
Nike Plus web site is up; my FuelBand Plus doesn’t crash the site anymore
Fuelband SE got reset & reinitialized. It is up & running (Unfortunately we can’t say the same about me ;o( I am static & typing)
One good side effect – In the process I discovered where Nike has the manual for FuelBand SE. The links are not that obvious.
Team USA’s win was very impressive – coming back from an 8-1 deficit, with 2 penalty points, and winning with 8-9 was spectacular, indeed!
Naturally corporate leaders everywhere are asking the questions – How did the team achieve this against an equally versatile and talented opponent? What heroic efforts did the team take ? How did the leads motivate the team to stage such a win ?
5. Reengineer & Redesign your equipment, processes & services based on real-life learning’s from the marketplace
During Race 5, Oracle had started strong and was leading but fell apart as it rounded the leeward mark and tanked on the upwind leg
They made a few design changes to the 130 foot tall wing sail, adding more curvature down low and more twist on the top.
The fine tuning of the win trim control was done over time, incorporating the learnings from the races
4. Reduce to the bare essentials (ie keep your drag minimum)
The team realized that they need to reduce the drag. They shorted the central spine of the boat, removing the bowsprit to reduce drag
3. Regroup & Rethink (ie Sail the boat differently)
The biggest change, came from the crew
We started sailing the boat differently
Even we were surprised how the subtle changes were so significant in the performance of the boat. But we changed the way the guys actually sailed the boat. It took us three or four days to do that properly
Even after making the structural changes after Race 5, the team lost 2 more races. … but they were a little more confident with our speed
They achieved a knot more speed after the changes. But the most improvement was the learning by the team that created the winning spirit
We were able to keep learning. In the end, that was the deciding factor
1. Winning Spirit, Confidence & Pep Talk
The Skipper (Jimmy Spithill) maintained confidence that a come back is possible
Kinley Fowler, one of their sail trimmers who had injured his back and couldn’t race, entertained the crew each morning with his own Australian brand of pep talk.
Lots of laughs at difficult moments !
And that worked ! Lessons, we all can incorporate into our corporate projects !