3 ways in which artificial intelligence will improve the world
The consequences of recent artificial intelligence developments (AI) have been a global stimulus for heated debate. As science fiction becomes reality, AI products slowly infiltrate homes and jobs.
This raises concerns about the potential negative effects of IA on the employment market, or even of the risks of AI uniqueness, when sensitive robots take over the world and destroy people.
Although these discussions are all valid, I think that AI should not focus solely on cool home gadgets or process optimization and automation. Rather, AI can be used to rethink how we resolve the problems of the world.
AI can improve things such as health care, education, poverty and security significantly. AI machines can already do some very helpful things people will not be able to do today. If we use this to increase what people do well, AI can have a positive impact on society, business and society as a whole.
I call this with AI, not to replace it by scale the human mind. The brain of humans is the most elegant computer of all. We automatically and constantly process millions of sensory inputs to help us learn and react to our environment.
But only about 300 million pattern processors responsible for human thinking are present in the human brain. What if not just more data, but orders of magnitude more processing capacity were to complement all our amazing ideas? Imagine how every problem we have today would be replenished.
In addition, there is sufficient technology to begin exactly this even with today’s primitive forms of IA. The following examples show the magnitude of social impact possible by combining human skill and ingenuity in AI in a variety of industries.
1. Precision Medicine
An emerging approach for disease treatment and prevention that takes into account individual genetic variability, environment and lifestyle for each person is driving the implementation of precision medicine. Think of it as a kind of personalized medicine. Every annual brain tumor diagnosis takes about 25,000 people in the United States, for instance.
They could all traditionally be treated in the same way as a single-size approach to see what could work. Precision medicine allows doctors and researchers to predict better, in which group of people it is possible to work on the treatment and prevention strategies for a specific disease.
Much of the answers are already found in the enormous amount of medical data. Ayasdi uses AI algorithms like profound learning to better analyze their data by doctors and hospitals. Medical practitioners were able, through their work, to identify previously unknown subtypes of diabetes that could lead to a better understanding of treatments that could work better for certain patient groups. Enlitic and IBM use similar AI algorithms but more accurately and efficiently detect tumors in radiological scans, and possibly speed up cancer cures.
In 2015 there were some 707 million breaches of cybersecurity and in the first half of 2016 554 million breaches. The effects of a few of these attacks, such as foreign governments which may prejudice US presidential elections, are truly frightening. Security teams today struggle to work with the increasing number of alerts produced by traditional instruments. AI’s auto-learning and automation capabilities can make us safer from terrorism or even less identity theft, and can improve efficiency and cost reduction.
AI-based solutions already available on the market can be more proactive and can prevent pre-execution attacks by identifying malicious content patterns and anomalies. For advanced global menace detection, Secureworks uses the predicting capabilities of AI. For fraud prevention and for endpoint safety such as smartphones and laptops, SiftScience, Cylance and Deep Instinct are using it. These technology will expand the reach and scale of safety professionals significantly and enable them, I hope, to detect threats well before they attack.
3. Precision Farming
In the next 30 decades, the worldwide population is expected to increase considerably, but our food production capacity will be difficult to keep pace. In our current farming methods AI drives efficiency to increase production and reduce waste without harming the environment. Systems such as John Deere’s AutoTrac allow large machinery to plant cultivation plants much in a far more consistent and precise manner and can reduce overlap in agricultural processes, such as tilling, planting and fertilisation.
Cainthus, a vision engineering firm, has a different approach. With deep learning, a facial recognition system has been created which enables individual cows to be tracked with minimal human involvement by their facial characteristics in six seconds only. They will soon be able, on the basis of their body shape, to detect earlier signs of lameness in a cow and thus alert farmers. As sensors on farms and drones proliferate to capture in real time images of the condition of vast quantities of agricultural land, the AI machines will help farmers to foresee the possibility that their crops and farms will need over a year beforehand.
Much more problems and markets can be covered by AI. In fact, a fundamentally new approach to each problem should be considered. These decisions will be taken by people who are interested in changing and improving the world and are now able to reach ever widening borders.