AI has been around for many years. Launched as a field of research more than 60 years ago, AI has now applications in many areas, from online services to industry and healthcare. Let’s take a look.
AI chatbots and assistants
Multiple tech companies have developed AI chatbots and virtual assistants that are intended to make people’s lives easier. At their very core, AI-powered chatbots facilitate communication between users and devices, most often via text-based commands. In general, chatbots are programed to provide specific replies to specific questions or statements.
More advanced, virtual assistants – embedded in desktop computers, smartphones, smart speakers, and other IoT devices – can perform Internet searches, manage calendars, control media players, etc. Most of them act on voice command; the activation is triggered either by keyword (like ‘Hey Google’ for Google Assistant) or after the user taps an icon (as with Siri on Mac computers).
Some of the most advanced AI assistants are embedded in smart homes systems: They allow users to control home IoT-powered devices simply by voice (controlling music volumes, turning on the heating system, opening the garage door, etc) Apple’s Siri, Amazon’s Alexa, Google Assistant, and Microsoft’s Cortana are some of the most famous and widely-used examples of AI virtual assistants.
As virtual assistants are empowered with new capabilities and are more and more used by the mass-market, issues of privacy and data protection come into focus. What happens with the data collected by these assistants? Is this data stored locally only or transferred to companies and used for other purposes?
Online services and applications
Internet companies increasingly rely on AI to improve their online services or design new ones. AI algorithms are behind search engines, social media platforms, and online stores, among others. For example, Twitter is using AI to improve users’ experience, Google operates a job search engine that is based on AI algorithms, and Microsoft has a range of AI-based intelligent applications (from Calendar.help to the AI chatbot Zo).
AI is also used by Internet platforms to identify and remove hate speech, terrorism content, and other forms of harmful online content. Researchers are exploring new algorithms that could be more efficiently used in content-control policy, as well as reduce the risk of bias and the possible negative consequences on the freedom of expression.
Researchers have been working on improving the accuracy of translation tools by using AI. Examples include Microsoft Translator and Google Translate. The World Intellectual Property Organization (WIPO) also developed an AI-based tool to facilitate the translation of patent documents.
Internet of Things
AI and the Internet of Things complement each other. AI provides ‘thinking’ for IoT devices, making them ‘smart’. These devices, in turn, generate significant amounts of data – sometimes labelled as big data. This data is then analysed and used for the verification of initial AI algorithms and for the identification of new cognitive patterns that could be integrated into new AI algorithms.
The interplay between AI and IoT can already be seen in multiple applications. Examples include smart home devices able to learn users’ preferences and adapt to their habits, vehicle autopilot systems, drones, smart cities applications, etc.
Scientists are continuously looking at new ways in which AI and IoT can work together. A team at the Massachusetts Institute of Technology (MIT), for example, has developed a chip that could enable IoT devices to run powerful AI algorithms locally, thus improving their efficiency.
The policy implications of AI and IoT-powered applications cover issues such as privacy, data protection, cybersecurity, and cybercrime.
Cybersecurity and cybercrime
As cyber-threats become increasingly complex, AI has the potential to assist organisations in dealing with cybersecurity and cybercrime challenges more efficiently. AI techniques and AI data analytics assist cybersecurity professionals in understanding cyber-threats and related risks better, allowing them to respond faster and with more confidence. AI is also used in detecting breaches, threats, and possible attacks, as well as in devising responses to such risks.
AI applications range from tools that can help catch spam and other unwanted messages on social networks or in e-mails, to algorithms that can help protect systems and networks as complex as the CERN grid.
The use of AI in authentication, identity, and access management solutions is increasingly relevant, some of which involve the scanning and recognition of biometrics (such as fingerprints, retinas, and palm prints).
Many tech companies are developing AI-based cybersecurity applications and platforms, and, as more and more start-ups are launched in this field, innovative solutions are developed on a continuous basis. At the same time, cyber criminals are also turning to AI to speed-up their game: They can rely on AI to test and improve their malware, and devise malware that is immune to existing cybersecurity solutions.
Autonomous systems (cars, weapons, etc)
Several tech companies (e.g. Google’s Waymo, Uber) and automobile manufacturers (e.g. Audi, Ford, Tesla) are working towards enabling autonomous cars powered by AI systems. Their ultimate objective is to develop fully autonomous vehicles based on systems that are able to completely control a vehicle without any human intervention.
At the moment, the technology has advanced to allow what is known as ‘high automation’ – the vehicle can perform all driving functions autonomously, under certain conditions, and the driver may have the option to control the vehicle. As per the automation levels developed by the Society of Automotive Engineers, this is indicative of level 4 automation, one step away from full automation. Companies such as Waymo, General Motors, and Uber are already deploying level 4 autonomous vehicles in some cities (particularly in the USA) as part of pilot projects. Because the testing and operation of autonomous vehicles has safety and security implications, authorities are increasingly moving towards introducing regulations – or at the very least guidelines – to govern these activities.
Read more about Autonomous vehicles on the dedicated page
The automotive industry is not the only one exploring the use of AI to bring in autonomous technologies. Drones powered by AI are no longer news, however they too have safety, security, and privacy implications. And the potential development of autonomous weapon systems raises concerns about their potential implications for humankind.
Healthcare and medical sciences
AI applications in the medical field range from surgical robots to algorithms that could improve medical diagnosis and treatment. In healthcare, big data and machine learning are improving diagnostic setting and the ability to establish customised treatments for different diseases and medical conditions.
AI is already used to improve and speed up the detection of diseases such as cancer, and tech companies are continuously working on developing new AI-powered tools that can assist in the early and accurate detection of medical conditions. Moreover, AI-powered devices are used to monitor a person’s health condition, and even caregiving robots are being developed to provide nursing services.
In medical research, scientists can now use big data, algorithms, and AI to explore and analyse vast amounts of data, improving their work and making it faster and more accurate. For example, researchers are using AI to develop anti-flu vaccines and to translate human brain signals into speech.
AI and robotics are the drivers of the fourth industrial revolution, especially as smart systems are increasingly being deployed in IT, manufacturing, agriculture, power grids, rail systems, etc.
Big data and AI could help factories better understand their processes and identify solutions to make them more efficient and reduce energy consumption. Some factories, for example, are already using AI to optimise their processes and adapt them to new circumstances, as well as to detect and to predict malfunctions in their equipment before they appear. Manufacturers can also use AI to test new ideas, with tools such as Autodesk and generative design. Moreover, the use of AI to improve process efficiency can also lead to reduced environmental impact and cutting waste.
AI applications in agriculture include autonomous robots that are able to harvest crops and to perform other agricultural tasks, AI-powered hardware and software that monitor and analyse crops and soil conditions (e.g. drones to collect data and AI techniques to analyse it), and algorithms that track and predict weather and other environmental conditions that can affect crops. In the energy sector, AI-powered robots are tasked with inspecting, repairing, and maintaining energy installations. Power grid operators are also using AI to analyse vast amounts of data to improve grid management and monitor the relation between electricity supply and demand. In the railway system, AI solutions are deployed for monitoring railway networks and assisting in maintenance operations.
There are many other examples of AI being used in various industrial sectors, and many more applications are being developed on an ongoing basis. As the industry increasingly relies on AI solutions, multiple policy issues are brought into focus, from the impact on the labour market to the need to protect AI-dependent infrastructures from cyber-risks.
AI is increasingly used by financial institutions like banks and credit lenders to make credit decisions. For example, algorithms and machine learning analyse different types of information to help decide whether to offer a loan to a potential customer. Improving predictions and managing risks are other areas where AI has proven to be useful for financial institutions. In 2017, for example, traders relied on analytical solutions provided by AI company Kensho to predict an extended drop in the British pound.
AI is also demonstrating growing efficiency in fraud prevention and detection. The technology is being used in credit card fraud detection systems: It relies on information about a client’s buying behaviour and location history to identify potential fraudulent activities that contradict their usual spending habits. In the banking sector, AI applications range from AI-powered assistants that help clients with tasks such as scheduling payments and checking balances, to apps that offer personalised financial advice.
Other uses of AI in the financial sector cover trading and investment banking activities (for example, for investment research purposes or for predictive analytics), underwriting (to predict whether a loan applicant is likely to pay back the loan), insurance services (e.g. automating claims processes for insurance companies or customising insurance policies), and authentication and identity verification (e.g. software that identifies a customer via facial or fingerprint recognition, in online banking systems or at ATMs).
AI holds a lot of promise in the education sector. AI tools are used by educational institutions to bring more efficiency into the performance of administrative tasks, to automate grading tasks (especially for multiple-answer tests), or to speed up admission processes. AI is also increasingly employed in the development of smart content. For example, Cram101, developed by Content Technologies, relies on AI to make textbook content more comprehensible to students by summarising chapters, providing flashcards and tests, etc.
Intelligent tutoring systems involve the use of AI to adapt the educational system to the characteristics and needs of each student. The Chinese-based company Squirrel, for example, focuses on helping students score better on standardised tests. Courses are divided into many small elements called knowledge points. For each point, there are video lectures, notes, examples, and exercises. Throughout the study process, the system determines the knowledge points that the student needs to focus more on and adapts the curriculum accordingly. Such a system is described as adaptive learning: It determines what students know and do not know and focuses on the latter.
Going a step forward, personalised learning aims to customise the learning process to not only what students know and do not know, but also to what they want to learn, and how they learn best. Personalised learning frameworks rely on AI to analyse vast amounts of information about students and to provide new content and learning experiences that meet the students’ specific profiles.
Some schools have started experimenting with virtual facilitators and intelligent tutors. For example, schools in Bengaluru, India use robots to complement human teachers. The robots are taught to deliver certain lessons and to respond to frequently asked questions from students. This, in turn, gives teachers more time to focus on the children and on more personalised learning.
As the potential of AI in education is continuously explored, questions are also raised. Is adaptive learning indeed useful? Or does it focus too much on standardised learning and testing, while not actually preparing students to adapt to the fast-changing world of work? Would students be better off learning via intelligent platforms or from robots, or would they miss the interaction with human teachers?
AI is increasingly used in the public sector, in public administration, law enforcement, judicial systems, etc. Due to its ability to process vast amounts of information and identify connections between data sets, AI brings more efficiency in administrative processes, helping to improve the provision of public services. Smart virtual assistants are already used by public authorities to improve interaction with citizens – examples include Latvia’s UNA and Singapore’s Ask Jamie.
Parliamentary processes can also benefit from AI tools. The Indian Parliament, for example, has embarked on a journey to use AI for more efficient data processing and for simplifying and improving legislative work.
Law enforcement agencies also rely on AI in some of their work. For example, facial recognition technology can help them identify criminals. Judges and courts may turn to AI in the hope that it would help them issue more consistent decisions or make the justice system cheaper and fairer. But things do not always go as planned and unintended consequences are poised to appear, as using AI can also lead to biased and discriminatory decisions.
AI applications in the entertainment sector are numerous and cover the movie industry, sports, games, and fashion, among others. Customised user experience is one illustration of AI used in these sectors. Netflix, for example, relies on machine learning to suggest movies that its users likely want to watch. The personal styling service Stitch Fix uses data and algorithms to pick clothing items and accessories that match its customers’ style and preferences.
Using AI in designing clothes is a reality as well, as demonstrated by Glitch, a company founded by two computer scientists. In the gaming industry, AI is used to create a more enjoyable player experience. In sports, the technology has multiple applications, from assessing the performance of players and predicting fatigue and injuries, to optimising broadcasting and advertising activities.
AI is also starting to be used in audiovisual content production. In the movie industry, for example, IBM’s Watson and its underlying machine learning techniques were used to develop the trailer for 20th Century Fox’s movie Morgan, while McCann Erickson Japan developed an AI-powered creative director to direct the production of TV commercials. US-based Digital Domain uses AI to produce advanced visual effects for movies, while Belgian company Scriptbook claims its AI algorithms can predict whether a film will be successful by analysing the script. Flow Machines and Amadeus Code employ algorithms to assist artists or amateurs in creating music.
AI’s potential in content production also generates concerns and one increasingly relevant example is that of deepfakes – the use of AI to create fake video and audio recordings which could be used for malicious purposes. There are also questions about the use of AI in personalising user experiences, particularly the question of choice: If we simply rely on ‘recommendations’ made by content streaming platforms or online stores, to what extent are our choices really personal?