Artificial Intelligence: From Past to Future

Opinion studies show that even top business leaders lack a detailed sense of AI and that many ordinary people confuse it with super-powered robots or hyper-intelligent devices. Today, in general, people think of AI as “machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment, and intention”. But what exactly is AI? Artificial intelligent algorithms are made so that they are able to make decisions, most of the time using real-time data. Using digital data, sensors, or indirect inputs, they examine the material spontaneously after combining data from a lot of sources and then act on the insights derived from those data. While these systems adhere to significant human preferences, there is tiny risk of AI going crazy or threatening human beings. Computers can be intended while interpreting data in ways that help humans work at a higher level. However, if the software is inadequately sketched or based on fragmentary or biased knowledge, it can threaten humanity or replicate former transgressions. Since most people don’t know what AI is, how it works, what it can do, and what has it already done, a little knowledge of its past, present, and future make it very easy to understand.

While everyone seems to be talking about artificial intelligence these days, it’s good to remember that this is not something new. In ancient Greece, Homer wrote of Talos - a man forged in bronze by the lame god Hephaestus. He was the world’s Talon on a myth but one of the epic proportions where inanimate metals became an autonomous tool; fast forward one and a half thousand years and in the valley of Iran, Arabian inventors began to automate the taming of the elements but it was after Gutenberg gave us the printing press that everything stepped up a gear. Against the backdrop of the industrial revolution, British inventors Charles Babbage and the world's first programmer Ada Lovelace began laying the foundations of automated calculation. Our second leap forward harnessed oil, gas, electricity, and vast factory assembly lines, but in the war effort automation had to get smarter and after Alan Turing cracked the Enigma code, things started getting digital. The success of the Dartmouth conference gave AI its name and a mission. Processing speed started to double annually and the golden years of AI discovery began to dawn. But winter was coming and as AI was hit by setbacks, investors lost faith and funding dried up. Business interest took over, funding the tech that revolutionized their industries but with the boom came bust. Finally, with advances in natural language, computer storage, and processing power AI began to achieve some of its long-standing goals - even playing chess rather well. In the last 20 years, the Internet of Things has connected our belongings to everything else. Amazon's EC2 introduced cloud computing to store our data online. ImageNet brought machine learning algorithms to bear on understanding the real world and GANN network technology has allowed machines to learn unsupervised. Today, AI tests are medicine to treat our patients. It's revolutionizing every industry including the financial and retail industries - remaking the wheel and driving with these developments seem to come faster and faster.

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Today, people have misunderstood AI. We expect it to take over, but the reality is that AI is still many years away from becoming sentient. Instead, what we are seeing today are devices like Amazon's Alexa, Google's Home, or Apple's Siri. These gadgets may sound sentient, but, in fact, they are far from it. The AIs that we see and interact with are not optimized to be independently intelligent, but they’re optimized to be our guides, assistants, and extensions of our memories. Besides consumer electronics, artificial intelligence’s greatest promise may be in transforming entire industries. The underlying technology making this happen is known in the technological community as supervised learning. You have humans to be able to provide the training data for the algorithms to then automate a task. For example, a self-driving car knows how to avoid a pedestrian - that’s how to stay in a lane - because humans have sat down with images taken from the front of these cars and said “this is a human”, “this is the lane that marks the edge of the road” and it’s these thousands of thousands of hours of human feedback which ultimately enables a self-driving car to be trained to automate this. Google’s Waymo is at the forefront of self-driving car technology and is using supervised learning to drive it forward. But, in fact, transportation is just one area that is booming in the field of artificial intelligence. Another is healthcare, Arterys - their machines have been trained on thousands of patient images which gives physicians and radiologists better information for diagnosing cancer in the brain and lungs and also detecting dangerous heart conditions. A radial address not only has to be highly trained to really look at the organ, but they also spend a lot of time sometimes just drawing contours by hand and that wastes a lot of time. So, what they’ve been doing is automating that by training the system on thousands of images where they know what the answer is. That’s a real quantum leap because the system can really propose a suggestion to the physician that is really close to what he or she would have done. Agriculture is also seeing a big change in the use of artificial intelligence. A see-and-spray machine by Blue River Technologies uses artificial intelligence to be able to detect which plant is a weed and which is a crop. The machine goes through the field very fast, it’s a very wide machine and on the back is a camera that is looking down at the ground. It looks at every single plant 50 times per second every camera that it has is looking at every plant and deciding whether it is a crop or weed and they’re applying herbicides only to the weed and nowhere else and by doing this we can reduce about 95% of the herbicides being used. They have a database that has grown really fast and now it’s close to a million images that they’ve collected across many different farms and many different geographies that gives them the variability that the algorithms need in order to be trained and reliably detect which ones are weed and which ones are crops. From farming to the food that you shop from AI is impacting the whole supply chain. At Bossa Nova Robotics, robots scan the aisles of the supermarket aiding retailers in logging grocery inventory. Of course, robots that can roam the aisles at the supermarket or tractors that can automatically graze crops are just the beginning. Experts believe a profound technological shift is underway.

The adoption of AI is accelerating and primarily this is because we have so much more data now and we have the computing power to actually do something with it. It doesn’t take days to run your predictive model. It takes hours. Sometimes even minutes. The risk for companies that don't use AI is that they will be left behind. In the new way of thinking when you have an AI, when you’re collecting data, when you do the product, when you ship the product, or how do you bring the value? Public perception can be another challenge that can hinder the uptake of AI. AI can be an enabler It can shift jobs to new higher skills, more productive types of roles because AI is simply a tool that people can use in a way that’s appropriate. Many aspects of it didn’t necessarily look so different from what we see today, but will simply get better. Silicon companies like Intel and ARM are now designing and making chips where the AI training algorithms are embedded into those chips in hardware and so you see performance factors of one hundred or thousand and so. What we’re seeing in some summary is much more computing power available to perform all the math that’s needed. There are some potential challenges that may hinder the advancement of AI, for example, data privacy concerns and also how do we protect the personal information, medical information, your financial information and in the meantime still can help you with building the algorithm with your critical information or personal data, but not really leverage it. And so, we worry that a computer is going to somehow drive my car for me and what happens if the computer is wrong or what happens if the computer makes a decision different than the decision I would make. It’s difficult to understand artificial intelligence. It’s a complicated field, but if you can create tools that put in the hands of the decision-makers and make it simple enough for them to use and simple enough for them to understand then it becomes a very valuable tool.

In fact, all aspects of AI’s future impacts are in our hands. “Artificial intelligence can lead us down many different paths, some good and some bad”, — Toby Walsh, Machines That Think. “Society must choose which path to take”, — Maria Temming. Through these kinds of safeguards, societies will increase the odds that AI systems are intentional, intelligent, and adaptable while still conforming to basic human values. In that way, countries can move forward and gain the benefits of artificial intelligence and emerging technologies without sacrificing the important qualities that define humanity.