Artificial Intelligence: A Force Disrupting Many Sectors
Several factors have impacted the proliferation at which the research and development of AI is progressing. The increase in computational resources, the explosive growth of data which stands at 48% year-on-year, based on Mary Meeker’s 2017 Internet Trends Report, along with the decreasing cost of data storage, and the surge of open source frameworks, in addition to the shift from broad AI to industry-focused AI, have enabled Machine Learning (ML) and Deep Learning to accelerate the evolvement of AI.
Today, different stakeholders are betting on the capacities of AI in transforming the world as we know it. In fact, Global Forecast 2022 report expected the AI market to be worth $16 BN by 2022, which will evidently increase the investments in the technology.
AI is already on the track of empowering different sectors. In this article we will be underlining the main sectors where AI is already having or is projected to have the greatest impact: automotive, healthcare, education, and banking. WHAT DOES AI STAND FOR? Known as the father of Artificial Intelligence, English mathematician John Mccarthy, coined the term "Artificial Intelligence" in 1956 when he held the first academic conference on the subject. He presented his definition of the word at a conference on the campus of Dartmouth as such "the science and engineering of mak-
ing intelligent machines".
A number of dictionaries define AI to be an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. AI can rationalize and take actions that have the highest probability of achieving a specific goal. The technology bases itself on the idea that human intelligence can be mimicked by a machine that is wired using approaches from mathematics, computer science, linguistics, psychology, and more. It is important to mention that AI is not merely one technology; it is a group of correlated technologies including (1) natural language processing, that ensures a normal interaction between computers and humans, (2) ML, that allows computers to evolve once exposed to data, and (3) expert systems that are softwares programmed to provide advice.
ML algorithms are based on object track ing and sophisticated pattern recognition .
“Computer vision” constantly analyzes the environment and feeds perceived images into the algorithms. The images are then analyzed and the nature of the objects is classified through AI. These algorithms give the vehicle ‘ intelligence’, allowing the vehicle to learn object characteristics such as movement, size and shape in order to classify future images with higher accuracy.
AI enables cars to communicate with one another and with the road infrastructure. By handling back-end computations, AI will deliver accurate and timely data, whereas ML algorithm will be tracking and registering data related to the vehicle’s speed, location, and destination. AI will learn the driver’s daily schedule, the roads they usually take, and their habitual stops to provide the driver with insights before their commute.
These systems include the most inno-vative in-vehicle features like speech recognition and virtual assistants.
Speech recognition provides an easier way for humans to interact with technology and in this case, providing the interaction between drivers and their cars. How does it work? AI interprets voices as sound waves that are converted into code which the algorithms then analyze. Further to that, the speech is compared to other samples stored in the cloud to determine what the user is saying. The speech recognition software will immediately start to update speech samples the more the driver uses it, taking into account how specific words are pronounced and the tone of the driver’s voice. The technology’s capability of learning a distinct accent and pronunciation of words also provides outstanding accuracy and precision. AI also helps speech recognition technology recognize speech context and tone.
The advancements in speech recognition have paved the way for in-vehicle virtual assistants. At first, drivers were very limited with what they could do with speech recognition technology, but today, virtual assistants allow drivers to ask for directions, get general information and even adjust their seating position and A/C settings. By learning driver preferences, habits, routines and even tracking the user's location, route and destination, virtual assistants can make recommendations on-the-go. It can remind its users to pick up items on their way home, recommend restaurants in their area or even place their coffee order while they're on their way to their local coffee shop.
AI HEALTHCARE ASSISTANTS
AI health bots are able to cover a vast number of outpatient services; they will ask you about your symptoms and provide you with the information you need to know about your medical condition by looking into the outcome of past treatments, as well as your personal medical history. AI assistants can also sustain continuous monitoring and care to the patients who require that sort of attention, like in the case of mental healthcare. Moreover, bots can also communicate with patients on behalf of doctors to follow up on their progress, and revert back to the doctors with the feedback and information related to the patient’s recovery journey by using Natural Language Generation and Processing (NLG/NLP) technologies.
AI and ML are currently capable of understanding how the human DNA functions and impacts life. Systems such as Google’s Deep Mind and IBM’S Watson can digest immense amounts of data - like patient records, clinical notes, diagnostic images, treatment plans - and perform pattern recognition in a short span of time. By interpreting the human genome, ML can predict the molecular effects of genetic variation and identify patterns across millions of data points - a task that would take humans forever to do. ML algorithms can quickly scan a patient’s personal and family health records for similar patterns and come up with suggestions that can lead to an early detection, hence prevention, of a dangerous disease. With this process being put into action, medicine could detect dangerous diseases such as cancer and Alzheimer’s through very faint symptoms, which increases the survival rate or treatment options of the patient.
According to tech crunch, new drugs usually take 12 to 14 years to be available for commercial use. However, with AI/ ML applications on deck, the process is accelerated. Computers can mine patient biological data to understand why people survive diseases and apply the results they found to improve current utilized therapies, or create new ones.
INTELLIGENT TUTORING SYSTEMS (ITS)
Simulating a one-to-one human tutoring experience, ITS leverage AI to deliver learning activities that cater to students’ cognitive needs; it provides targeted and timely feedback. Many ITS use machine learning techniques, selflearning algorithms that aggregate and analyze large data sets, along with neural networks; this combination allows the systems to decide on the type of content that should be delivered to the learner. On another note, the tutoring systems that are model based utilize a number of AI-ED tools that tailor the learning experience to the student’s cognitive and affective states, allows them to discuss and question the subject being taught, and include open learner models that motivate the students by keeping them aware of their own progress, along with social simulation models that help the student understand the subject by understanding the culture and the social norms behind it.
INTELLIGENT VIRTUAL REALITY
Virtual Reality ( VR) is all about simulated immersive experiences. It creates an environment where learners get the chance to explore, interact, and manipulate certain elements. They are therefore capable of using these virtual experiences in the real world. Today, students can explore a nuclear power plant, wander through the streets of Ancient Rome, or orbit around the outer planets. Coupled with AI, VR becomes intelligent and delivers an optimized virtual experience. It offers an environment that can interact or respond to the student’s reactions. Intelligent synthetic characters are incorporated to the virtual world; they can play roles in setting that can be dangerous or unpleasant to the student.
INTELLIGENT COLLABORATIVE LEARNING
Collaborative learning has proven itself to be a rather effective method of learning, as it engages learners and motivates them. AI-ED technology supports many collaborative approaches such as :
1. Adaptive group formation : Coupled with data about each learner in the classroom, AI’S goal is to design a grouping of students that share the similar cognitive level and interests.
2. Expert facilitation: AI techniques provide collaboration patterns that are used as an interactive support to the collaborating students. For example, Markov modeling, an approach using the probability theory to represent randomly changing systems, identifies collaborative problem-solving strategies.
3. Virtual agents: They can act as an expert a coach or a tutor, a virtual peer (fellow innovative student), or someone the students have to teach themselves.
4. Intelligent moderation: Using machine learning and shallow text processing techniques, they help the teacher in analyzing discussions all the way to reaching a productive collaboration.
Banks engage with their clients either via in-person conversations in a branch, or through telephone calls with sales or service representatives. Although this sort of interaction has proven to be extremely effffffective, it also presents itself to be costly. Chatbots can be very helpful on that level, as they maintain quick and high-quality customer service, while plummeting banks’ expenses. In addition to the above, banks can better connect with the millenials by incorporating AI into their systems. As they are considered the largest users of mobile messaging services, such as Facebook Messenger, Whatsapp, and Snapchat, banks will have a wide open window of interaction with this generation and will expand its client-base and conversion opportunities. Moreover, the use of AI promotes user-tailored content; further to aggregating data related to their subject’s habits and needs, bank bots will be enabled to prompt notifications about new products that are available and also send more personalized messages. On another note, banks leveraging voice enabled chatbots add an extra layer of biometric security for their customers. The bot will recognize its users’ voice and therefore allows them to access to their account balance, set up account related alerts, pay bills, and report a lost card, among many other functions.
Robo advisors are digital platforms that provide automated and algorithm-driven financial planning services, and require little to no human intervention. As any Ai-focused system, robo-advisors collect information related to their clients’ financial situation and future goals, and then employs the aggregated data to offffffer advice and/or automatically invest client assets.
Today, robo advisors are capable of performing sophisticated tasks, such as tax-loss harvesting, investment selection, and retirement planning, all at lower costs and greater investment outcome. According to Investopedia, client assets managed by robo-advisors is projected to surge to $2TN, after having reached $60BN in 2015’s Q4. Other than being an easily accessible tool that is available 24/7, robo-advisors offffffer a great advantage - they are low-cost alternatives to traditional advisors. By eliminating costs related to human labor, they can offffffer high-quality service at a fraction of the cost. In fact, most of these online platforms charge an annual flat fee of 0.2% to 0.5% of the client’s total account balance, that compares to a rate amounting to 1% to 2% charged by a .human financial planner However, despite its proven efficiency, the robo-advisor is still nascent technology. Although it has automated some functions related to asset allocations, portfolio management, and more, 40% of bank users would not be comfortable using this tool alone in the times of extreme market volatility, as stated by Investopedia and the Financial Planning Assosiaction’s recent study.
By leveraging AI instruments, banks can identify and prevent fraud and security hacks in real time. If a customer is using a debit/credit card, the detection engine can score transactions within 0.3 seconds, and then flag fraud or approve genuine transactions without any interruption of the purchase process.
AI and ML technologies can crunch a massive number of transactions and flag any anomalies; they are able to learn from one instance, and therefore improve security. Some banks are also employing these technologies to payment providers, supporting security operations throughout the entirety of the payment ecosystem. These technologies’ algorithms can identify patterns in the data to recognize fraudulent claims, and by learning from each case, they can automatically assess the severity of damages and predict the repair costs based on historical data, sensors, and images. Ai-driven tools can prevent false positives and provide a better detection in place, which enables fraud investigation teams free to perform tasks of higher value.
By allowing AI to the banks back-office operations, many labor-hours will be scratched offff the employees’ task-list. AI technologies can review loan agreements, identify repayment patterns, and bring Robotic Process Automation to populate data entry and increase processing speed, specifically in the case of structured data.
AI algorithms are set to be more precise given their exposure to great amounts of data. For instance, some banks are planning to include non-banking data in the loaning process, such as Amazon interactions, social media communications, and sensors in phones such as GPS and accelerometers, to therefore unearth new ways to determine the creditworthiness and provide the adequate financial help to their users.