Day one was introduction-and-ice-breaker day at Parc 55, the lovely San Francisco hotel in which Anita Borg scholars were housed. I had the opportunity to also meet winners of other scholarships such as the American Indian Science and Engineering, Hispanic College Fund, Lime and United Negro College Fund Scholars. I had an amazing time meeting this many geeks in one place and having discussions in the hotel about issues dear to our hearts such as vi vs. emacs and Mac vs. PC.

During dinner at the hotel, we met Marissa Mayer, Vice-President of Location and Local Services at Google. She shared her thoughts on recent advances in image recognition made in the larger field. Nowadays, researchers would like to do image recognition using only a person’s face (or even or portion of it) instead of also looking at other features such as the color of their clothes in different pictures and matching them. She also touched upon text translation and how it has significantly improved over the last few years. Now we can even translate texts from a right-to-left language to an up-down language. After dinner, I caught up with Marissa Mayer to get thoughts on Google Plus:

Q: How is Google Plus a different social network?

A: What we have done with Google Plus is design a system that we feel is a better model of real-world interactions. In the real-world, you don’t say everything to everyone all the time. We really feel that it’s important to decide who you share something with and we have achieved that with the concept of Circles. So, you can assign people to categories such as family and close friends. You can decide how broadly you want to share things with circles and we think that is one big advance.

Q: What is Google Plus’ differentiating factor?

A: We have interesting features like the video chat Hangouts which are very popular, but the biggest differentiating factor is your ability to control what you share and to scope sharing in a way that matches circles.

Q: What would you say about the privacy policy of Google Plus?

A: We give people a lot of control on what they share and our model feels more natural since it reflects what they do in everyday life.

Q: How does it feel to be a woman at Google?

A: I don’t think of myself as a woman at Google but as a geek at Google! So it’s a great place to be at if you’re a geek like me. I love trying out the latest gadgets and talking with my colleagues about things like 3D modeling and that’s really what brings us together.

On day two, we set out to Google Headquarters in Mountain View. Once there, we found cheerful Googlers and a colorful campus. There were gardens maintained by Googlers as part of their hobbies and amidst those garden, you could also see sculptures and, of all things, mini bicycles. These are used by Google employees if they need a quick ride from one building to another.

Then we had a series of presentations. One of them was by James Gosling, the creator of Java. Since Java is the first language most of us are introduced to, I was thrilled to have the opportunity to have a chat with James Gosling:

Q: Why is Java considered good as an introductory programming language?

A: There are two sides to Java. One is that it is pretty easy to learn; you can start with baby steps. The other is that when something goes wrong, it fails early and reasonably predictably.

Q: What about using C++?

A: With languages like C or C++ the problem is that when something goes wrong, it usually manifests itself in a very strange way and it is very difficult to figure out what is going on. When you are teaching, being able to have something that is comprehensible is good.

Q: Some people are suggesting using Pascal as a first language. What do you think?

A: In a sense, Java is a lot like Pascal in that it was also used in teaching. The problem with Pascal is that once you learn it, what can you do with it? The role of Pascal was limited to a teaching language. What is interesting about Java is that it works both ways: it can serve as a teaching language and you can use it to get a job! Once you learn the basics of Java, you can do many exciting things like incorporate graphic libraries and databases.

One thing that I should mention is food.

Google has many cafeterias on campus. Each has its own theme: Chinese, Indian, Healthy, Not-so-healthy! At lunch, I had the opportunity to speak with Robin Jeffries, who focuses on UI at Google. She had known Anita Borg in person so we had the chance to learn more about the woman who inspired this scholarship. Anita Borg created Systers, a mailing system to keep women in computing connected with each other and encourage more women to enter the field. Robin is now the “chief cat herder” for Systers and she thus continues Anita Borg’s endeavour to promote computing to women.

To conclude the day, we had dinner at Bocce Café, a fancy Italian restaurant in San Francisco. Both the food and the discussions with Google employees were delicious.

Day three was highlighted by the scholar’s poster session. It was our time to shine and showcase our research. I saw very diverse projects within computer science and engineering and this was another great chance to get to know each other’s passions in greater detail.

Our feeling at the end of the retreat was unanimous: we had to stay in touch. Thus, we had a brainstorming session with scholars and organizers to find ways to stay connected as we are spread across the continent. The Google Scholar’s Retreat is an intelligent endeavour to bring technologically-driven students with various skills and expertise together.

Overall, I had the chance to meet and talk to many Googlers while at the retreat and all were eager to share their experience at Google. “We work hard but we play hard” seemed to be a common motto

Based on the work of Dr. Leon Chua, the memristor (a.k.a. memory resistor) remembers its state even after being turned off, so that a computer’s memory is still accessible right after it is turned on again.  You can imagine turning on your computer and not having to wait several minutes before the operating system is loaded in memory. Looking further ahead into the future of memristor-based machines, imagine an “intelligent” computer that would “understand” the tasks you are trying to perform and would help you do them more efficiently, even by giving you smart advice (although it would have to be more sophisticated than Microsoft Office’s infamous Paper Clip assistant). Wouldn’t that save you time and effort?

Achieving that goal is not a long way ahead. So far, non-volatile memory modules were developed using memristors that store up to 100 gigabits (12.5 GB) in 1 cm2, whereas conventional flash memory stores up to 16 gigabits (2 GB) in the same amount of space. However, the speed of memristor-based memory is currently ten times less than DRAM. As this is the first prototype, there is still room for improvement.

The memristor’s resistance depends on the direction the current takes; the resistance increases in one direction and decreases in the other. Therefore the resistance R is a function M of charge q passing through, i.e. R = M(q). But the key aspect is this: when the current stops flowing, the memristor stores the last resistance state and thus starts over at that same resistance once the current starts flowing again. The memristor developed at HP labs is made of titanium dioxide thin films which makes it extremely small, thus reducing power consumption and production costs. Also, when connected together, memristors have a shape similar to that of artificial synapses. These properties makes them attractive for building an artificial brain. “This new circuit element solves many problems with circuitry today—since it improves in performance as you scale it down to smaller and smaller sizes,” said Dr. Chua. “Memristors will enable very small nanoscale devices to be made without generating all the excess heat that scaling down transistors is causing today.”

Numerous research groups have worked on memristors and proposed several applications. But recently, HP labs revolutionized the application of memristor by building a neuromorphic chip which is similar to a biological system as it brings data and its computation  at the same location. In fact, in the brain, the actual data we perceive–say an image–is brought in through synapses and translated to vision at that same synaptic area. The memristor chip is basically a multicore microprocessor where each core has direct access to its own memory thus eliminating wires and large power consumption. This brain-like microprocessor will run MoNETA (Modular Neural Exploring Traveling Agent), software in development since last November at Boston University’s Department of Cognitive and Neural Systems, that will allow the machine to perceive its environment and opt for choices that will guarantee its survival. Thus, it will mimic the survival aspect of humans and animals which forms the basis of our evolution.

Currently, Boston University and HP are developing the perceptual, navigational and emotional systems which will simulate the behavior of a small mammal using hardware.  This simulated nervous system will learn through plastic changes in synaptic connections (similar to biological neurons). This will allow it to interact intelligently with its environment: searching for food, following learned paths, avoiding predators, etc. In the near future, we can thus expect to see burgeoning projects give rise to artificially created small animals that have almost all the capabilities of their biological peers

REFERENCES
IEEE Spectrum: Artificial Intelligence