What is Intelligence?

How do you define intelligence? You might consider it to be the ability to:

• Learn, understand or to deal with new or trying situations (Merriam Webster)
• Acquire and apply knowledge and skills (Oxford)
• Understand and learn well, and to form judgments and opinions based on reason (Cambridge)

Since the invention of the computer, people have strived to create powerful machines that can imitate human intelligence. Computers generally do what we tell them to do, but a machine that can make its own decisions is incredibly powerful. One way to approach building such machines is to “teach” them using virtual games (like chess) since they have defined rules and strategy, which the computer can understand and evaluate.

The history of Artificial Intelligence and game theory starts with IBM’s Deep Blue, a chess-playing machine developed in 1997. It was incredibly powerful and could process 200 million moves a second. Deep Blue gained notoriety by beating Gary Kasparov, one of the greatest human chess players of all time, in a 6 match tournament.

IBM built this machine by programming the rules of chess into its system and gave it some basic guidelines, such as the weight of each piece (eg. a rook is more valuable than a pawn). It also had a pre-programmed memory of millions of chess positions, such as opening and closing strategies. However, Deep Blue was not intelligent. Blue could compute the optimal move given any board setup, searching its own library of millions of position, and its game was always reactionary. It didn't out-think Kasparov, it simply out-calculated him.

To illustrate: even though a sports car or jet plane can move much faster than any human, we do not consider it to be athletic. It simply has the correct parts that allow it to achieve such speed. Similarly, chess-playing machines relied on raw power rather than decision making. They did not understand the game or learn from their mistakes.

There have been other chess playing machines since Deep Blue, all of which approached the game in a similar way. However, a revolutionary new machine appeared last year that took a new approach. DeepMind, a company partnered with Google, created a machine called AlphaZero. This machine used deep neural networks to process its moves, rather than a data bank. It was only taught the basic rules of the game (piece movement, check/checkmate rules), but it wasn't given information on the weight of the pieces or any pre-programmed moveset.

Instead, the machine played millions of games against itself. After every game, it would decide which moves where beneficial and contributed to victory. This process, known as reinforcement learning, allowed AlphaZero to develop its own standards for move evaluation and strategy. After a few short hours, it had uncovered many of the most popular strategies that have taken years for humans to discover.

AlphaZero was matched up with Stockfish, a chess machine similar to DeepBlue (albeit exponentially more powerful). Out of 1000 games, AlphaZero won 155 and lost only 6. It was also able to beat other machines playing Shogi (Japanese chess) and Go. in short, AlphaZero destroyed its competition.

Kasparov, who witnessed the beginning of chess machines, commented on the

effectiveness of AlphaZero, saying that it “prioritizes piece activity over the material, preferring positions that to my eye looked risky and aggressive.” He also noted that the style that AlphaZero plays with mirrored his own, calling it “dynamic and open.”

AlphaZero could mark the beginning of intelligent machines, using programs that can learn over time to become more effective. There is still much to be discovered in regards to the usefulness and shortcomings of these machines. As they are integrated into our everyday lives, we will have to decide if computers are truly intelligent, and if so, how we should use them responsibly.

Questions:

1. Do you consider AlphaZero to be “intelligent”?
2. What other functions do you think artificially intelligent machines could perform?

Biotechnology and Gene-Editing

One of the most controversial types of technology is biotechnology. It has may positives attributes that include improving the quality of life for those who are born with disabilities, eradicating diseases, and even strengthening the nutritious density of agriculture. However, it could have dangerous implications.

In recent years, gene-editing has been expensive, labor intensive, and inaccurate causing it to be infrequently thought about as a realistic option for regular consumers. That is about to change with the creation and improvement of CRISPR/Cas9.

CRISPR is a form of biotechnology that uses the immune systems of bacteria to cut and alter the genes of other organisms based on programmed artificial genetic code. It has become more cost effective and faster to do than traditional methods. According to an article on Vox, some key implementations of this biotechnology include:

1.     Editing crops to become allergen free, live under extreme-weather conditions, or survive devastating diseases.

2.     Preventing or eradicating genetic diseases. Scientists have begun research to change the structure of DNA so that it changes the BRCA genes (found to increase risk of breast cancer) and the mutations that cause cystic fibrosis. There is already research showing that this technology can create a gene to be immune to HIV. Read more on that here. However, occasionally a misfire could lead to cancer, so heavy precautions needs to be taken before this can be used safely.

3.     Creating new and stronger antibiotics. With antibiotic resistance on the rise, this new technology can help create more antibiotics cheaper than before.

4.     Gene Drive. This biotechnology works with CRIPSR to choose which genes are passed down or survive and which ones don’t. This has the potential to eradicate the mosquitoes that carry malaria.

5.     Finally, and potentially most dangerously, creating designer babies. The ability to edit human genes is great when talking about reducing the risk of certain cancers or building immunity for life-threatening diseases. However, that also gives us the power to pick and choose which genes are desirable and lends the ability to design what a person could look like.

This theory of adapting or augmenting the human genome isn’t something new. In 1957, the idea of transhumanism gained traction in the science-fiction community. The movement focuses on “the use of technological advancements to enhance our physical, intellectual and psychological capabilities, ultimately transcending the limitations of the human condition.” As much as it was science-fiction then, it is now real, possible, and has potential to become accessible. The questions lie in what we choose to use this new technology for.

In reference to being asked about how humans and technology interact in the future, science historian at Vanderbilt, Michael Bess says, “I think each of us needs to ask, “What does it mean for a human being to flourish?” These technologies are forcing us to be more deliberate about asking that question. We need to sit down with ourselves and say, “As I look at my daily life, as I look at the past year, as I look at the past five years, what are the aspects of my life that have been the most rewarding and enriching? When have I been happiest? What are the things that have made me flourish?”

Questions for reflection:

1)    Do you believe that biotechnology has the ability to make our lives more rewarding and enriching? Why or why not?

2)    Do you think the pros and cons are equal for gene-editing?

3)    Do you think CRISPR has the ability to interfere with the human condition?


Ted Talks for more additional thought:
One of the creators of CRISPR 
Technology and the human condition 

Can Big Data Predict Your Future For You?

Big Data is an emerging field that has impacted every industry in America. This impact has stretched to education, and Georgia State nursing school has began to use Big Data to predict student success. They were surprised to see that your performance in an introductory Math class would be a good predictor for your success as a junior or senior. Studies showed that less than 10 percent of students that earned a C in that math class graduated, while 80% of students with a B+ graduated.

This has led data analysts at Civitas Learning to conclude that students are much less likely to graduate if they got less than an A or B in a foundation course for their major. This article by the New York Times cites University of Arizona as having uses for big data. Studies from the University of Arizona have shown that a student's grade in English comp is crucial to graduation. In the U of A, 41% of students that received a C in English comp graduated, while 61% of students that received a B graduated, and 72% of students that received an A graduated. Conclusions like these can allow colleges to decide where to deposit resources to ensure higher graduation rates. If studies like this prove causality between performance in a certain class and graduation rates, it is not far fetched to assume more resources will be thrown at the students that aren't performing well in select classes.

It is incredibly interesting to see the long term benefits that universities can encounter if they use big data. The Insite Center for Business Intelligence and Analytics in the U of A MIS department is experimenting using big data on current freshmen. They are measuring social interactions, such as whether freshmen go to the gym, where they buy their food from, and even when they buy clothes from the bookstore. They are putting all this data into algorithms to measure the likelihood that a freshman would make it to sophomore year. This attacks the idea that comfort with a student's environment could impact how successful they are academically. Insite has tracked 30,000 students over the past three years, and have cited an accuracy rate of 85%!

Of course this information could be use in a negative manner, which makes security of this information of utmost importance. It is imperative that this information not land in the wrong hands, but rather be used to advance the student's goals and aspirations.

I have a couple questions regarding this topic for you all!

1. Do you believe that every day interactions within a campus can have an impact on how successful a student is?
2. Would you like big data to predict your future for you? Such as your likelihood of graduation, best major for you, or even how you should interact with others on campus.
3. Do you think that big data can be abused? How?