Artificial Intelligence (AI) and Machine Learning (ML) techniques have held a promise to transform our lives for a long time. While early research in AI techniques date back a few decades, thanks to recent innovations and advances in hardware and other related technologies, the business world is finally getting serious about its disruptive potential.
Tech analysts will remember 2017 as the year when business world finally started getting serious about the disruptive potential of AI. The tech Oligopoly – Apple, Amazon, Facebook, Google and Microsoft – announced large investments and began showcasing AI solution offerings for consumers and businesses. 2018 is also likely to see startups, tech-service firms and the academia addressing the skill shortage.
“Artificial intelligence (AI) is the intelligence exhibited by machines or software. It is also the name of the academic field of study which studies how to create computers and computer software that are capable of intelligent behavior.” – Wikipedia
AI and ML tools using intricate algorithms and emerging computing techniques along with big data and analytics are being applied to solve real-world problems. Applications of AI range from high-skill tasks such as recognizing cancer in X-ray images to lower-skill tasks such as recognizing text in images.
Image credit: Charis Tsevis
The promise of A.I. systems: supplementing “Thinking Jobs”
In a Harvard Business Review article, Megan Beck and Barry Libert explain “There are just a lot of things that machines can do better than human beings, and we shouldn’t be too proud to admit it. Many skilled jobs follow the same general workflow:
- Gather data
- Analyze the data
- Interpret the results
- Determine a recommended course of action
- Implement the course of action
This holds true in a number of occupations. Doctors perform tests, analyze the results, interpret the results to make a diagnosis, plan a course of treatment, and then work with the patient to make this treatment plan a reality.
Financial advisers gather and analyze data about their clients and potential investment vehicles, interpret the implications taking into account factors such as risk tolerance, recommend an investment strategy, and help their clients carry this strategy out over time.
Lawyers understand the specific challenges faced by their clients and review case history, prior judgements and other references to argue a case in favor of their clients.
What this means is simple: AI techniques may be able to enhance the capabilities of professionals engaged in ‘thinking’ jobs while replacing some of the low-end white collar jobs.
Application of Artificial Intelligence Techniques
Artificial Intelligence is not a single technology, but rather a collection of techniques that are applied to solve problems. The challenges and opportunities addressed by the “AI family” are varied:
- Machine Learning (M.L) applications – Machine learning is the science of getting computers to act and learn by recognizing patterns without being explicitly programmed. M.L techniques evolved from the study of pattern recognition and computational learning theory in Artificial Intelligence. Practical application of ML techniques include:
- Medical applications – automating transcriptions using voice recognition, reading and tabulating X-rays, AI databases to aid decision making by doctors and Surgeons.
- Financial analytics and forecasting – personalizing financial services through analytics-driven recommendation engines, robo-advisory services in the wealth management
- Security agencies, Immigration and customs departments are beginning to use ML and image recognition software to identify images and videos.
- Deep Learning Platforms – Deep learning is a fast growing field in AI that combines ML techniques with hardware optimized for AI and Graphics processing units (GPU) architected to efficiently run AI-oriented computational jobs.
- Technologies with thought provoking names like Deep Blue from IBM, DeepMind from Google or Microsoft’s Chatbots are going beyond press-mentions and vaporware to solving real world problems.
- Deep learning platforms are being used for speech recognition, image recognition/Optical character recognition and other areas requiring high-compute.
- Virtual Agents and Intelligent Agent (IA) – These AI applications use animated, virtual characters to serve as an online customer service representative.They can also be programmed to be autonomous entities which observes through sensors and act upon an environment using actuators. Emerging applications include:
- Chat-bots and Apps that can engage in ‘intelligent‘ conversation with users, and respond to their questions and act on their simple requests.
- Smarter Assistants. Applications like Siri, Alexa and Cortana have entered the consumer market in a big way. While consumers become comfortable using these Apps on the smartphones, companies are enhancing these techniques to enable customer self-service and more advanced business applications.
- AI and ML used with other emerging technologies- In many cases, AI and ML is used in conjunction with other technologies including big-data and analytics, robotics and other digital techniques. A few examples include
- Automating advertisements and analysis of advertisement’s efficiencies
- Fraud detection and prevention – automated fraud detection, as well as anti-money laundering and anti-terrorist financing compliance monitoring
- Bots, and automated tools to aid cyberdefense and cyber security, predictive cybersecurity monitoring and response systems.
Tech companies including Amazon, Google, IBM and a number of startups and government agencies are investing heavily in A.I technology platforms and solutions. Emerging opportunities in AI field include
- AI and ML Consulting Services – Many organizations are interested in solving specific problems using AI, but realize they lack the in-house expertise or skills. To address this challenge, software and technology service companies are launching AI focused consulting services. Google’s service is called “Machine Learning Advanced Solutions Lab,” and Amazon recently relaunched consulting services under its “ML Solutions Lab.”
- AI research and development – Large organizations are continually looking for help in identifying areas where they can pilot emerging technologies. This includes areas where machine learning and domain specific application of the algorithms can be applied. Such R&D efforts are also supplemented by external consulting services (highlighted above)
- AI Software development. End user organizations begin their AI initiatives by leveraging commercial platforms like IBM’s Watson and design and configure their requirements on top of these. Specialists with knowledge of these platforms who can help with the program management and testing are in demand.
- Education and training – Universities and technical schools around the world continue to introduce AI courses and programs. This has opened up opportunities for researchers and corporate trainers around the world.
Government and Social Policies
Governments are recognizing the strategic importance of A.I. Last year, the White House released a report (link) on the ways that artificial intelligence will transform our economy over the coming years and decades. The report adds that ‘although it is unlikely that machines will exhibit broadly-applicable intelligence comparable to or exceeding that of humans in the next 20 years, it is to be expected that machines will continue to reach and exceed human performance on more and more tasks.’ The report suggests that policymakers should prepare for primary economic effects including:
- Positive contributions to aggregate productivity growth;
- Changes in the skills demanded by the job market, including greater demand for higher-level technical skills;
- Uneven distribution of impact, across sectors, wage levels, education levels, job types, and locations;
- Churning of the job market as some jobs disappear while others are created; and
- The loss of jobs for some workers in the short-run, and possibly longer depending on policy responses.
Governments and policy makers around the world continue to review and define their own policies.
In a report published a few months ago, The Chinese government makes its AI ambitions clear: the country wants to become the world’s leader in AI by 2030.
- Computer Science and Artificial Intelligence Laboratory – MIT
- What is Artificial Intelligence? Stanford.edu
- Artificial Intelligence Is Almost Ready for Business – Harvard Business Review
- The Promise and Limitations of Machine Learning – Ruslan Salakhutdinov, Carnegie Mellon University
- The age of analytics: Competing in a data-driven world – McKinsey report – New machine-learning and deep-learning capabilities have an enormous variety of applications that stretch into many sectors of the economy. Systems enabled by machine learning can provide customer service, manage logistics, analyze medical records, or even write news stories.
- Artificial Intelligence, Automation, and the Economy – White House report
- Top 20 Companies paying the Highest salary for A.I Engineers – AI Skill shortage leads to high salaries
- At Sundar Pichai’s Google, AI Is Everything —And Everywhere -“Building general artificial intelligence in a way that helps people meaningfully—I think the word moonshot is an understatement for that,” Pichai says, sounding startled that anyone might think otherwise. “I would say it’s as big as it gets.”
- Wall Street’s Big Brother: The AI Software Goldman Sachs And Steve Cohen Are Using To Track Traders
- GE acquires Wise.io to deepen its machine learning stack – GE Digital today announced that it has acquired Wise.io, a machine-learning powered service that helps businesses find patterns and trends in their vast data stores.