AI winter is a period of ‘reduced funding and interest in the field of artificial intelligence.’ AI winters are preceded by hype cycles and ambitious claims of what AI can do. Money into research and AI companies pours in and expectations are inflated. But it doesn’t last and after a while, pessimism takes over the community and spreads to press, investors and government. Budgets are slashed, funding is stopped and AI research virtually dries up. There have been two AI winters: first one in the 1970’s and the next in the 1980’s.
We are there again as far the hype is concerned. There has been no shortage of buzz around AI in the past few years. Everyone, everywhere is talking about AI and how it can predict revenues, increase sales, create chatbots who can do natural language conversations like just like real customer service people.
Is the boom going to end soon? Andrew Ng, chief scientist at Baidu research and a prominent figure in the AI community doesn’t think so. The advancements in computing power and availability of huge amounts of training data are providing “the fuel required to make emerging AI techniques feasible.”
“There are multiple experiments I’d love to run if only we had a 10-x increase in performance,” Ng adds. For instance, he says, instead of having various different image-processing algorithms, greater computer power might make it possible to build a single algorithm capable of doing all sorts of image-related tasks.
Addressing concerns about hype, Andrew Ng says:
“There’s definitely hype,” adds Ng, “but I think there’s such a strong underlying driver of real value that it won’t crash like it did in previous years.”
Andrew Ng have good reasons to be optimistic. So let’s look at the other side of the coin and some recent AI misses. Let’s consider the case of IBM Watson and claims that it’s going to eradicate cancer:
It was one of those amazing “we’re living in the future” moments. In an October 2013 press release, IBM declared that MD Anderson, the cancer center that is part of the University of Texas, “is using the IBM Watson cognitive computing system for its mission to eradicate cancer.”
Well, now that future is past. The partnership between IBM and one of the world’s top cancer research institutions is falling apart. The project is on hold, MD Anderson confirms, and has been since late last year. MD Anderson is actively requesting bids from other contractors who might replace IBM in future efforts. And a scathing report from auditors at the University of Texas says the project cost MD Anderson more than $62 million and yet did not meet its goals.
In one of the many Watson ad campaigns IBM ran, Watson tells Bob Dylan that he has read all his lyrics and that the meaning of Dylan’s music is all about ‘time passing and love fading.’
Really? I am a child of the 60s’ and I remember Dylan’s songs well enough. Ask anyone from that era about Bob Dylan and no one will tell you his main theme was “love fades”. He was a protest singer, and a singer about the hard knocks of life. He was part of the anti-war movement. Love fades? That would be a dumb computer counting words. How would Watson see that many of Dylan’s songs were part of the anti-war movement? Does he say anti-war a lot? He probably never said it in a song.
IBM Watson is a good product and I won’t be so harsh on IBM. Their ads and marketing efforts got lots of people and companies interested in AI. A colleague of mine told me that his CEO rolled his eyes (metaphorically speaking) when he told him to use machine learning to make predictions about customer behavior and revenue a few years ago. While he will never know for sure if it was because of IBM PR, but the CEO certainly changed his mind recently recently and started to think of AI as a crystal ball that will transform his business and bring millions and millions of dollars in revenue.
AI is not magic. As a small example, I used Watson to build a gaming chatbot with AI that could hold ‘meaningful’ conversations with users on a small number of pre-decided topics. Watson uses Natural Language Processing and Machine Learning algorithms to understand user messages and requires lots of training. We weren’t able to get it to converse at a level of a 2 or 3-year old and it could be easily confused. In the end, we decided to use a cloud based API called Wit.ai which gave us pretty much the same results as Watson.
Chatbots with AI replacing humans to provide customer service is a long, long shot. That doesn’t mean that chatbots or AI isn’t useful. Even if chatbots can increase 1%-2% of customer service efficiency for a major enterprise or a large internet company and help their customers troubleshoot simple issues or prescreen before passing off to a live agent, it will still be massive improvement. Richard Socher, chief scientist at Salesforce said:
“If we were to make the 150,000 companies that use Salesforce 1 percent more efficient through machine learning, you would literally see that in the GDP of the United States,” he says.
Hype wasn’t the only reason for the last two AI winters, although it did play a big hand. It initially fueled the interest but failed to live up to the expectations and didn’t provide real value to corporations and government. While the recent hype has outpaced the reality, many companies are using AI to translate its predictions and forecasts into actions to grow revenue with positive ROI. In the mobile gaming domain, publishers and studios are experimenting with dynamic pricing which applies machine learning to price the same item differently to different users based on how much it predicts each user will be willing to pay. For a large gaming company, even a slight increase in revenue over traditional models such as segmented or A/B pricing translates into millions of dollars of revenue.
Is an AI winter coming? AI won’t out-think humans anytime soon, understand deep meaning of music, eradicate cancer, or replace customer service people entirely, it has started to provide real value in many domains and incremental improvements. AI is not magic and the hype will die down, but the next AI winter will be more like a California winter, not a Canadian one.
In “First, Break All the Rules: What the World’s Greatest Managers Do Differently” the authors, Marcus Buckingham and Curt Coffman, have put together their observations from more than 80,000 Gallup interviews they conducted with various leaders and managers over a period of 25 years. The book is full of excellent insight into what great managers do and don’t do and debunks several traditional management myths. One such myth is that people are capable of almost anything if they work hard enough or everyone has unlimited potential. According to the authors, this is a complete fallacy and while it is an uplifting thought, it is far from reality.
“There once lived a scorpion and a frog.
The scorpion wanted to cross the pond, but, being a scorpion, he couldn’t swim. So he scuttled up to the frog and asked: “Please, Mr. Frog, can you carry me across the pond on your back?”
“I would,” replied the frog, “but, under the circumstances, I must refuse. You might sting me as I swim across.”
“But why would I do that?” asked the scorpion. ”
“It is not in my interests to sting you, because you will die and then I will drown.”
Although the frog knew how lethal scorpions were, the logic proved quite persuasive. Perhaps, felt the frog, in this one instance the scorpion would keep his tail in check. So the frog agreed. The scorpion climbed onto his back, and together they set off across the pond. Just as they reached the middle of the pond, the scorpion twitched his tail and stung the frog. Mortally wounded, the frog cried out: “Why did you sting me? It is not in your interests to sting me, because now I will die and you will drown.”
“I know,” replied the scorpion as he sank into the pond. “But I am a scorpion. I have to sting you. It’s in my nature.”
In this old parable, the frog made a fatal mistake in believing that scorpion’s nature will change.
Great managers reject this out of hand. They remember what the frog forgot: that each individual, like the scorpion, is true to his unique nature … They know that there is a limit to how much remolding they can do to someone. But they don’t bemoan these differences and try to grind them down. Instead they capitalize on them. They try to help each person become more and more of who he already is.
Under the same situation, different people react differently according to their nature. People are motivated differently. For example, I worked with a software developer who was very competitive by nature and his productivity would go through the roof when he hears that someone else on the team did it better or faster. That was his trigger. If a task carries too much risk, it is best assigned to the person who is meticulous than to someone who is a risk taker.
Everyone has some talents which the authors define as ‘recurring patterns that could be applied productively.’ Willpower is a talent. So is empathy and competitiveness. They key is to select and hire for talent and cast in the right role. This includes identifying an individual’s talents and assigning responsibilities which maximizes the strengths and neutralizes weaknesses.
Casting for talent is one of the unwritten secrets to the success of
great managers. On occasion it can be as simple as knowing that your
aggressive, ego-driven salesperson should take on the territory that re
quires a fire to bel it beneath it. And, by contrast, your patient, relation
ship-building salesperson should be offered the territory that requires
This may sound like common knowledge but I’ve seen it all too often where hiring managers put excessive emphasis on skills and knowledge over talent. Skill or how-to’s of role can be taught. Talent cannot be taught. A Java software developer can learn Python. An aggressive, ego-driven person generally makes a poor team player but put that person in a situation that requires a fire to be lit under it, and you may find a great fit.
People don’t change that much.
Don’t waste time trying to put in what was left out.
Try to draw out what was left in.
That is hard enough.
Beyond a person’s mid-teens, that unique network of synaptic connections, in which some are strong and robust and others non-existent, does not change significantly. This means that a person’s recurring patterns of thought, of feeling and of behavior do not change significantly. If he is empathic when he is hired, he will stay empathic. If he is impatient for action when he is hired, he will stay impatient.
There is also criticism. Dr. Tomas Chamorro-Premuzic suggests that focusing too much on our strengths may actually weakening us:
it’s important to understand that even the smartest, brightest, and most brilliant individuals have a dark side. They have certain elements of their personality, of their typical behaviors, that are quite counterproductive. And if those tendencies are left unchecked, no matter how smart, competent, and talented they are, their careers at risk of derailing.
Think of an employee or an individual who is very driven and ambitious. If we developed their ambition and drive even further, they might just become greedy. Or somebody who is very socially skilled, if they develop their social skills even further, they might become almost Machiavellian and manipulative. People who are very creative can become odd and eccentric, and people who are already a little bit confident, if we make them even more confident, they might become arrogant or overconfident.
I generally agree with the idea of focusing on strengths as I have seen too many managers focusing on irrelevant weaknesses or non-talents of their reports. There isn’t enough time to change an employee’s nature even a little or to give birth to a new talent. Does this mean we should completely ignore weaknesses? If the weakness is relevant and it is affecting performance, the manager must determine if the weakness is trainable (i.e. missing skill), the person is casted in the wrong role or whether the person can be paired up with someone who has complementary strengths. Either way, poor performance should be tackled head on as soon as possible.
Don’t get me wrong. It is a marvelous piece of engineering that gives us the reliable data transmission guarantee that other protocols don’t provide. Reliable data transmission between two devices on the internet is no walk in the park and TCP uses a lotofmagic under the hood to make things happen. Generally, it does a fine job of abstracting away low level details and its default settings work fine for most general purpose use cases. However, once in a while, things don’t go according to plan and we need to pop open the hood and do some tweaking. It is in these situations, that some knowledge of TCP comes in very handy.
Clustering or cluster analysis is the process of dividing data into groups (clusters) in such a way that objects in the same cluster are more similar to each other than those in other clusters. It is used in data mining, machine learning, pattern recognition, data compression and in many other fields. In machine learning, it is often a starting point. In a machine learning application I built couple of years ago, we used clustering to divide six million prepaid subscribers into five clusters and then built a model for each cluster using linear regression. The goal of the application was to predict future recharges by subscribers so operators can make intelligent decisions like whether to grant or deny emergency credit. Another (trivial) application of clustering is for dividing customers into groups based on spending habits or brand loyalty for further analysis or to determine the best promotional strategy.