On Intelligence: How a New Understanding of the Brain will Lead to the Creation of Truly Intelligent Machines

I am fascinated my the brain and all things neuroscience. 

On of my favorite books on the brain and what I can do is: On Intelligence: How a New Understanding of the Brain will Lead to the Creation of Truly Intelligent Machines.

This book was written by  Palm Pilot-inventor Jeff Hawkins with New York Times science writer Sandra Blakeslee.

Hawkins develops a powerful theory of how the human brain works, explaining why computers are not intelligent and how, based on this new theory, we can finally build intelligent machines.

What is the big idea?

Hawkins’ basic idea is that the brain is a mechanism to predict the future, specifically, hierarchical regions of the brain predict their future input sequences. Perhaps not always far in the future, but far enough to be of real use to an organism. As such, the brain is a feed forward hierarchical state machine with special properties that enable it to learn.

Here is one of my favorite snippets from the book called altered door experiment:

When you come home each day, you usually take a few seconds to go through your front door or whichever door you use. You reach out, turn the knob, walk in, and shut it behind you. It’s a firmly established habit, something you do all the time and pay little attention to. Suppose while you are out, I sneak over to your home and change something about your door. It could be almost anything. I could move the knob over by an inch, change a round knob into a thumb latch, or turn it from brass to chrome. I could change the door’s weight, substituting solid oak for a hollow door, or vice versa. I could make the hinges squeaky and stiff, or make them glide frictionlessly. I could widen or narrow the door and its frame. I could change its color, add a knocker where the peephole used to be, or add a window. I can imagine a thousand changes that could be made to your door, unbeknownst to you. When you come home that day and attempt to open the door, you will quickly detect that something is wrong. It might take you a few seconds’ reflection to realize exactly what is wrong, but you will notice the change very quickly. As your hand reaches for the moved knob, you will realize that it is not in the correct location. Or when you see the door’s new window, something will appear odd. Or if the door’s weight has been changed, you will push with the wrong amount of force and be surprised. The point is that you will notice any of a thousand changes in a very short period of time. 

How do you do that? How do you notice these changes? 

The AI or computer engineer’s approach to this problem would be to create a list of all the door’s properties and put them in a database, with fields for every attribute a door can 5have and specific entries for your particular door. When you approach the door, the computer would query the entire database, looking at width, color, size, knob position, weight, sound, and so on. While this may sound superficially similar to how I described my brain checking each of its myriad predictions as I glanced around my office, the difference is real and far-reaching. The AI strategy is implausible. First, it is impossible to specify in advance every attribute a door can have. The list is potentially endless. Second, we would need to have similar lists for every object we encounter every second of our lives. Third, nothing we know about brains and neurons suggests that this is how they work. And finally, neurons are just too slow to implement computer-style databases. It would take you twenty minutes instead of two seconds to notice the change as you go through the door.

Check out Jeff Hawkins on Firing Up the Silicon Brain

Source: https://en.wikipedia.org/wiki/On_Intelligence

Roundup of AI predictions for 2019

Are you pulling a holiday stunt like me? My holiday stunt usually involves manically reading to “look ahead” for next year.

This year, I want to leverage all the brain power that is looking in their magic ball and telling us what becomes of AI and how do we will harness the power of data.

Without further ado…given below is the list of articles I am speed-reading.













5 real world examples on how Artificial Intelligence and Data Science are helping enterprises

Enteprises are beginning to leverage Artificial Intelligence (AI) and Data Science provide services and product for their customers. If you would like to kick start your data science career, checkout this bootcamp from Springboard – structured to fit into your life, guaranteed to get you a job.

#1 Hubspot – Predictive Leading Scoring

Services range from predictive leading scoring as in the case of HubSpot, that claims in its website to be able to provide lead scoring estimate on the likelihood to convert leads into customers. This scoring is made possible by a machine learning model that considers features on customer behaviors over time in order to design a persona. After persona creation, HubSpot would be able to provide insight on positive and negative attributes that influence leadsconversion rates.

#2 Kissmetrics – AI on predict churn rate

Another example is Kissmetrics that delivers a pipeline to “turn insight into sales” (Kissmetrics slogan). A pipeline that begins with integrating to data sources and legacy systems, passing through data visualization capability as a first stage about getting insight on customers, moving to clustering algorithms that are able to provide customer segmentation which can be thought of as a second instance of understanding potential sales. Based on the above mentioned magic, Kissmetrics provides recommendations on how to deliver tailored, targeted email and social network campaigns.

#3 DOMO – AI to provide biz insight from biz cloud

There are other initiative as DOMO with center of gravity on providing a platform that enables AI activity. In the case of DOMO, its mission is to offer seamlessly integration to data sources and legacy systems (independent of it is on premise or on cloud) to support dashboards creation, i.e. business intelligence capability. It is a fast-growing market that aims to address medium and small companies that are no longer willing to deal with a platform maintenance; so tthey can concentrate on their core business and develop market intelligence that takes advantage from DOMO service that puts together in the same page all the information needed for business operation.

#4 Lufthansa – AI as prediction tool to implement aircraft safety

From the realm of transportation industry, there are good examples too, on the application of AI as part of B2B strategy; airline companies as Lufthansa, Flybe, KLM, BA just to cite a few; OEMs such as Airbus, Embraer and Boeing and MRO (maintenance, repair and overhaul) companies have invested on the application AI as a prediction tool to implement aircraft safety critical equipment prognostics and health monitoring. These companies have been taking advantage of the tons of data produced in every flight on the aircraft operation to unlock the potential of this data to help with maintenance optimization, reduction of ownership cost and increase of operational efficiency and safety.

#5 Airbnb – Data science and AI to derive models on pricing, customer segmentation and sentiment analysis

Airbnb has invested significantly in data science and AI to derive models on pricing, customer segmentation and sentiment analysis aiming to provide support to their users to ensure optimal assignment of people looking for accommodation to proper host. Every time a search is performed in Airbnb website, machine learning algorithms come into play to estimate the probability of assignment between guests and host. Airbnb also deploys AI algorithm outcomes to support guests on picking the best place considering historical data on their profile and hosts relating to the price to offer and improvements recommendation given guests feedbacks.

5 real world stories on how Artificial Intelligence and Data Science are helping humanity

#1 On the wings of analytics

Aircraft manufacturers, like Boeing, Airbus and Embraer have been developing data driven technology to predict critical aircraft equipment remaining useful life. This data drive prediction technology has been disruptive from a safety perspective given that in the ideal scenario there would not be unscheduled maintenance or unforeseen failure events that could cause the occurrence of disruption occurrence affecting thousands of passengers flying over the world. Machine learning has been massively applied to flight data and failure history databases, in order to predict flight equipment degradation.

#2 I, robot lawyer

The advent of cognitive computing came up with new possibilities on the automation of activities that depends on the knowledge and experience of a human about a specific domain. Ross is an automatic question-answering system that allows lawyers to research in natural language (voice and texting) on tons of documents and get a passage of interest. Ross became the world’s first AI attorney.It is powered by IBM Watson, a cognitive computing platform that enables machine learning application on legal data.

#3 I, robot doctor

Watson, IBM cognitive computing platform, has been deployed to diseases diagnostics for cancer, diabetes and cardiovascular diseases. This has been used mainly in US and also a recent initiative has been started in Brazil to recommend better treatment for cancer. Watson has been taught on those diseases symptoms from documents; tests have been successful in US. Watson learns and reviews its knowledge frequently based on verified and validated information from doctors, documents and books.

#4 AI and Breast Cancer

Imagine a 40-years old woman that realizes a lump in her breast. Her clinician takes a tissue sample and sends it to a pathologist.  Upon reviewing tissue sample pathologist will essentially define her future with their diagnosis, determining whether she has cancer or not. The work of pathologist is kind of like finding black cars on a city satellite image. Pathologists do this with tissue for hundreds of patients every day, looking for cancerous cells in each. Startups in US, Europe and China are developing AI-based technology, as AI-based image diagnosis, that assists pathologists in making rapid and accurate diagnoses for every patient, every time. That way patients can receive the best possible treatment and live the best possible life. AI-based technology is also building solutions to help identify patients that benefit from novel therapies, to make scalable personalized medicine a reality.

#5 AI to the rescue

Crisis management is an important issue for humans  who have to deal with natural calamities such as floods, inclement weather, earthquakes and so on. Intelligent systems to deal with events of crisis as aforementioned have been developed for institutes such as Fraunhofer to help in deployment of rescue resources for assistance, based on information from government about city occupation and infrastructure and information from social media on the occurrences. A derivative of crisis management is disruption management in airline operations, big companies as Sabre and Amadeus, along with startups as Masdima have been developing smart solution to help airline make decision whenever a disruption happens, taking into account passengers needs and resources availability such as crew, aircraft and airport.

These are a few of my favorite Data Quotes

In God we trust. All others must bring data.
~ W. Edwards Deming, statistician

Data is the new oil.
~ Clive Humby

The world is one big data problem.
~ W. Edwards Deming, statistician

Data beats emotions.
~ Sean Rad, founder of Tinder

Data that is loved tends to survive.
~ Kurt Bollacker, Data Scientist

Data is the next Intel Inside.
~ Tim O’Reilly

Data is the sword of the 21st century, those who wield it the samurai.
~ Jonathan Rosenberg

Errors using inadequate data are much less than those using no data at all.
~ Charles Babbage,  inventor

No great marketing decisions have ever been made on qualitative data.
~ John Sculley

I’m a bit of a freak for evidence-based analysis. I strongly believe in data.
~ Gus O’Donnell, economist