Artificial intelligence is an evolving field with tremendous potential to change the world. Although in its infancy, it is revolutionizing several industries ranging from cryptography to self-driving cars. Like any promising new technology, it has garnered its share of critics concerned about the threat it poses to human race.
According to a latest survey, 85% of Americans use at least one of six products with AI elements. Robotic process automation tools, like UI path, are making the monotonous and mundane manual work a thing of the past. This trend is prevalent in banking, information technology and manufacturing to name a few. Technology titans like Google and Tesla are pioneering the field of autonomous cars. Language is no longer a barrier with Google translate. Alexa and Siri already made their way into the American household. AI-driven business intelligence tools, like IBM Watson, are widely adopted in the large corporations to analyze complex datasets.
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Potential Threats and Counterarguments
First Threat: Job Joss
AI can take over several manual jobs in various industries. Any job involving repetitive, routine work is prone to automation. As an example, a bank teller's job can be completely automated. This trend can cause largescale unemployment.
Counterargument
On the contrary, experts claim that AI will create more jobs. According to World economic forum, 58 million new AI related jobs will be created by 2022. An example of an AI driven job is skilled data technician, a position that involves sorting and labeling the information fed into algorithms while watching for bias, predicts Colin Parris, vice president of software and analytics at GE Software Research. PwC also predicts job gains in robotics and information technology: in sectors in which new AI technologies boost demand through increasing income and wealth, and in fields that require a human touch, such as health, education, and personal services. Moreover, AI proponents expect that the AI transition to be smooth and not devastating. In 1979, the three-point line was introduced to the game. From this point on, the same players on the court had to change their strategy in order to shoot from longer distances more accurately. The players had to learn new skill sets in order to operate within the new rules of the game. Fast forward to today, the three-pointer has become a critical component of the game. The same analogy holds good for AI.
Second Threat: Technological Singularity and Misaligned Intelligence
The popular notion is that AI can become exponentially smarter leading to technological singularity. As a result, super intelligent AI can annihilate the mankind for the slightest divergence from the human goals. Alphago, an AI algorithm developed to play the board game 'Go' has defeated the 18-time world champion Lee Sedol. An advanced version Alphago Zero defeated Alphago in a span of 40 days of reinforcement learning. The input was only the basic rules of the game with no historical data.
Counterargument
Although this is a plausible threat, it can be averted with advanced research in AI safety and risk governance controls. Mckinsey analytics emphasizes on placing requisite controls depending on the complexity of the algorithms, their data requirements, the nature of human-to-machine (or machine-to-machine) interaction, the potential for exploitation by bad actors, and the extent to which AI is embedded into a business process. Conceptual controls, starting with a use-case charter, sometimes are necessary. So are specific data and analytics controls, including transparency requirements, as well as controls for feedback and monitoring. The right balance between risk and innovation should be achieved to prevent the catastrophe. The ability to assess the risks and to engage workers at all levels in defining and implementing controls will become a new source of competitive advantage. Implementing AI selectively in certain low-risk industries is another pragmatic solution. In other words, a carefully designed and rigorously tested self-driving car is less risky, compared to an AI robot with access to destructive weapons and nuclear arsenal. According to the survey, 89% of the road accidents are caused by man made errors. Self-driving cars can avert them completely. This alone can change the human commutation forever. Also, car-pooling using autonomous cars is an ecofriendly solution to minimize traffic congestion and automobile pollution. According to PWC study, AI's boost to the global economy by 2030 is approximately $15.7 trillion dollars which amounts to 14% increase more than the current economic output of India and China combined. Netflix saves $1 billion per year because of its AI algorithm which personalizes the movie recommendations to individual users (according to the journal ACM Transactions on Management Information Systems). According to Mckinsey global institute, Amazon reduced its 'Ship-to-click' time by 225% because of AI.
Third Threat: Inevitable Bias in Healthcare and Criminal Justice
A system is only as good as the data it learns from. According to the BBC survey, take a system trained to learn which patients with pneumonia had a higher risk of death, so that they might be admitted to hospital. It inadvertently classified patients with asthma as being at lower risk. This was because in normal situations, people with pneumonia and a history of asthma go straight to intensive care and therefore get the kind of treatment that significantly reduces their risk of dying. The machine learning took this to mean that asthma + pneumonia = lower risk of death.
Counterargument
Since so much of the data that we feed AIs is imperfect, we should not expect perfect answers all the time. Recognizing that is the first step in managing the risk. Decision-making processes built on top of AIs need to be made more open to scrutiny. Since we are building artificial intelligence in our own image, it is likely to be both as brilliant and as flawed as we are. Streamlining the input data with multiple quality checks should minimize the risk of misjudgment.
Conclusion
To drive home the point, a quick peek at two recent incidents bolsters the case for AI: Two AI robots in Facebook started communicating in their own language. Facebook recognized this quickly and dismantled them. On the contrary, Google translate AI program developed its own artificial language to translate between different pairs of languages although it was programmed for specific language pairs. This is a constructive leap towards perfection. Google proudly announced this amazing development.
“We can complain because rose bushes have thorns or rejoice that thorn bushes have roses”.
References
- https://www.independent.co.uk/life-style/gadgets-and-tech/news/facebook-artificial-intelligence-ai-chatbot-new-language-research-openai-google-a7869706.html
- https://www.newscientist.com/article/2114748-google-translate-ai-invents-its-own-language-to-translate-with/
- https://www.aceable.com/safe-driving/car-accident-statistics/
- https://news.gallup.com/poll/228497/americans-already-using-artificial-intelligence-products.aspx
- https://futureoflife.org/background/benefits-risks-of-artificial-intelligence/?cn-reloaded=1
- https://www.theatlantic.com/sponsored/pwc-2019/ai-building-which-jobs-will-become-obsolete/3148/?sr_source=pocket&sr_lift=true&utm_source=pocket&utm_medium=CPC&utm_campaign=simplereach&utm_source=PK_SR_P_3148
- https://www.hpe.com/us/en/insights/articles/ai-by-the-numbers-1908.html?chatsrc=ot-en&jumpid=ba_jf68u23pzx_aid-520000028
- https://deepmind.com/blog/article/alphago-zero-starting-scratch
- https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/confronting-the-risks-of-artificial-intelligence
- https://www.bbc.com/future/article/20161110-the-real-risks-of-artificial-intelligence
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