Artificial Intelligence(AI) refers to any human-like intelligence exhibited by a computer, robot, or other machines. In popular usage, artificial intelligence refers to the ability of a computer or machine to mimic the capabilities of the human mind — learning from examples and experience, recognizing objects, understanding and responding to language, making decisions, solving problems — and combining these and other capabilities to perform functions a human might perform, such as greeting a hotel guest or driving a car.
Artificial Intelligence is no longer restricted to the realms of Science Fiction and Research Labs. It contributed more than $ 2 Trillion to the economy last year and as per the PWC report, this number is set to reach $ 15.7 trillion by 2030. Artificial Intelligence touches millions of lives daily where it interacts with us through Smart Phone, Personal Computer, and other Smart Devices, It yields immense benefits across all the sectors ranging from Healthcare, Manufacturing, Transportation, Retail, Education, Information Technology, Marketing among several others.
AI vs. Machine Learning vs. Deep Learning: What’s the Difference?
These terms are often used interchangeably, but what are the differences that make them each a unique technology?
These technologies are commonly associated with artificial intelligence, machine learning, deep learning, and neural networks, and while they do all play a role, these terms tend to be used interchangeably in conversation, leading to some confusion around the nuances between them.
- Think of artificial intelligence as the entire universe of computing technology that exhibits anything remotely resembling human intelligence. AI systems can include anything from an expert system — a problem-solving application that makes decisions based on complex rules or if/then logic — to something like the equivalent of the fictional Pixar character Wall-E, a computer that develops the intelligence, free will, and emotions of a human being.
- Machine learning is a subset of AI application that learns by itself. It actually reprograms itself, as it digests more data, to perform the specific task it’s designed to perform with increasingly greater accuracy.
- Deep learning is a subset of machine learning application that teaches itself to perform a specific task with increasingly greater accuracy, without human intervention.
How do artificial intelligence, machine learning, neural networks, and deep learning relate?
Perhaps the easiest way to think about artificial intelligence, machine learning, neural networks, and deep learning is to think of them like Russian nesting dolls. Each is essentially a component of the prior term.
That is, machine learning is a subfield of artificial intelligence. Deep learning is a subfield of machine learning, and neural networks make up the backbone of deep learning algorithms. In fact, it is the number of node layers, or depth, of neural networks that distinguish a single neural network from a deep learning algorithm, which must have more than three.
Artificial intelligence applications
As noted earlier, artificial intelligence is everywhere today, but some of it has been around for longer than you think. Here are just a few of the most common examples:
- Speech recognition: Also called speech to text (STT), speech recognition is AI technology that recognizes spoken words and converts them to digitized text. Speech recognition is the capability that drives computer dictation software, TV voice remotes, voice-enabled text messaging and GPS, and voice-driven phone answering menus.
- Natural language processing (NLP): NLP enables a software application, computer, or machine to understand, interpret, and generate human text. NLP is the AI behind digital assistants (such as the aforementioned Siri and Alexa), chatbots, and other text-based virtual assistance. Some NLP uses sentiment analysis to detect the mood, attitude, or other subjective qualities in language.
- Image recognition (computer vision or machine vision): AI technology that can identify and classify objects, people, writing, and even actions within still or moving images. Typically driven by deep neural networks, image recognition is used for fingerprint ID systems, mobile check deposit apps, video and medical image analysis, self-driving cars, and much more.
- Real-time recommendations: Retail and entertainment web sites use neural networks to recommend additional purchases or media likely to appeal to a customer based on the customer’s past activity, the past activity of other customers, and myriad other factors, including the time of day and the weather. Research has found that online recommendations can increase sales anywhere from 5% to 30%.
- Virus and spam prevention: Once driven by rule-based expert systems, today’s virus and spam detection software employ deep neural networks that can learn to detect new types of viruses and spam as quickly as cybercriminals can dream them up.
- Ride-share services: Uber, Lyft, and other ride-share services use artificial intelligence to match up passengers with drivers to minimize wait times and detours, provide reliable ETAs, and even eliminate the need for surge pricing during high-traffic periods.
- Household robots: iRobot’s Roomba vacuum uses artificial intelligence to determine the size of a room, identify and avoid obstacles, and learn the most efficient route for vacuuming a floor. Similar technology drives robotic lawnmowers and pool cleaners.
- Autopilot technology: This has been flying commercial and military aircraft for decades. Today, autopilot uses a combination of sensors, GPS technology, image recognition, collision avoidance technology, robotics, and natural language processing to guide an aircraft safely through the skies and update the human pilots as needed. Depending on who you ask, today’s commercial pilots spend as little as three and a half minutes manually piloting a flight.
Key Benefits of Artificial Intelligence
1. Reducing Human Intensive Labour
AI has been instrumental in reducing human-intensive labor by leveraging on Smart Automation. As per the Oxford Economics Report in June 2019, more than 2.25 million Robots are deployed worldwide (A threefold increase from last decade). Now in many factories, all the heavy lifting, carrying, transporting, and other mundane activities are carried out by AI-enabled robots. This saves a lot of human efforts which can be better utilized in more productive activities.
Example: Amazon deploys more than 100,000 AI-based Kiva robots in their fulfillment centre. The use of AI-enabled robots not only reduces human efforts in performing physically intensive work like carrying large inventory quantities from one shelf to another but also enhances safety at the workplace. These Cyborgs can load and unload one full trailer of stocks in less than 30 minutes which took more than a couple of hours for human workers.
2. Increasing Efficiency in Pharma Industry
AI has been a boon to the Pharma and Healthcare Industry. As per the study by MIT, merely 13% of the drugs pass the clinical trial stages, further, it costs Pharma companies millions of dollars for any of its drugs to pass the clinical trials. Therefore Pharma companies in order to ensure better utilization of their R&D Budget deploy AI to increase the chances of their drugs clearing the clinical trials. Different Machine Learning algorithms aid scientists in finding the right composition of different salts in the drugs by analyzing historical data related to Genes, chemical reactions, and other attributes.
Example: Novartis, A leading Pharma Company, has been using Machine Learning Algorithm to find out which compound is best at fighting the diseased cells under examination. Previously, this procedure involved the manual microscopic investigation for each sample which was both time-consuming and prone to human errors. With Machine Learning based algorithms, they can run real-time simulations and get more accurate results sooner.
3. Transforming the Financial Sector
Most of the Financial Applications revolve around analyzing past data to get better results. There is no surprise that Artificial Intelligence whose USP is analyzing past data enjoys huge success in Finance Sector. AI has wide-ranging applications in the Finance Industry ranging from Risk Assessment, Fraud Detection, Algorithm based Trading, Financial Advisory, and Finance Management among several others
Example: Paypal has been using an advanced Deep Learning Algorithm to detect fraudulent transactions. Paypal processes a humongous amount of transaction data, it processed more than $235 billion in payments from 4 billion transactions done by more than 170 million users. Paypal uses a Deep learning algorithm to analyze the large scale of data and compare transactions with fraud transaction patterns stored in their database. Based on this pattern comparison it can detect fraudulent transactions from normal transactions.
4. Quicker and Easier Customer Service using AI Chat-Bots
An earlier version of Chat-Bots interactions was very time consuming and frustrating. The bots used to run into loops and could assist only in pre-defined tasks. The AI-powered chat-bots using Natural Language Processing have a better understanding of human interactions and can learn on its own and hence are far more adept in providing an adequate response to the customers.
Example: Bank of America virtual assistant Erica is one such example of an AI-enabled chat-bot. It has already helped 7 million clients since its roll out in June 2018. Erica uses Artificial Intelligence, Predictive Analytics, and Artificial Neural Network to serve more than 50 million client requests it received last year. The request ranges from normal banking tasks like Bank balance information, Bill Payment to complex tasks like Investment planning and budgeting suggestions.
5. Enhancing Safety on Roads
As per World Health Organization Report, more than a million people die in road accidents every year. Artificial Intelligence is playing a major role in reducing such fatalities. Many companies have started using AI to record and analyze every minute details regarding the driving pattern of different drivers ranging from lane discipline, Traffic rules abidance, distance maintained with other vehicles on the road. The details so collected is used by AI applications to provide safety recommendations to the driver and help automobile companies to come up with safer vehicles.
Example: Microsoft has been experimenting with HAMS (Harnessing Auto-Mobiles for Safety) to enhance safety in Indian roads. It takes into account two factors- the driver’s state and his/her vehicle’s position relative to other vehicles. It makes use of Front and Rear camera mounted in front of Driver’s seat. The front camera is used to gauge the driver’s physical state like fatigue by detecting eye movement and yawning frequency. These are detected using Mouth Aspect Ratio. Rear camera analyses lane discipline and distance with other vehicles. All this data is analyzed using AI applications using Edge-based processing and safety based recommendation alerts are generated in real-time.
6. Predicting and Enabling Quicker Response to Disaster
Artificial Intelligence has turned out to be a silver lining for us in the face of calamity. Nowadays, Artificial Intelligence applications are being deployed to pre-empt natural disasters using different pattern recognition algorithm. It is also being used to mitigate the losses after such disasters by aiding in disaster relief work. AIDR (Artificial Intelligence for Disaster Response) is widely used for this purpose.
Example: AIDR was deployed in rescue effort post the earthquake in Nepal (2015). Volunteers and rescue workers were able to reach out to the affected victims quickly with the help of AIDR. AIDR uses Social Media analytics to categorize all the tagged tweets. The insights from these tweets not only helped rescuers to reach the affected area quickly but also helped them in categorizing areas based on urgency to better channelize the rescue effort.
How exactly do MNC’s benefit from these technologies?
All the world’s tech giants from Alibaba to Amazon are in a race to become the world’s leaders in artificial intelligence (AI). These companies are AI trailblazers and embrace AI to provide next-level products and services.
Chinese company Alibaba is the world’s largest e-commerce platform that sells more than Amazon and eBay combined. Artificial intelligence (AI) is integral in Alibaba’s daily operations and is used to predict what customers might want to buy. With natural language processing, the company automatically generates product descriptions for the site. Another way Alibaba uses artificial intelligence is in its City Brain project to create smart cities. The project uses AI algorithms to help reduce traffic jams by monitoring every vehicle in the city.
How Alibaba Is Using Artificial Intelligence In Healthcare?
One major step in the direction has been the launch of ET Medical Brain by Alibaba Cloud in March 2017. The ET Medical Brain is a suite of AI solutions designed to ease the workload of medical personnel. The suite basically uses computers to act as virtual assistants for patients and in medical imaging, drug development, and hospital management; for instance, the development of AI-based tumor diagnosis systems.
Alibaba Health also recently unveiled its first AI service for medical diagnostics called ‘Doctor You,’ which can use imaging in early diagnosis of cancer.
Other projects include the partnership between Alibaba Cloud and Wuhan Landing Medical High-tech Co. on a system that leverages AI and visual computation technologies to detect early-stage cervical cancer by using cell cytology.
In 2016, the first large-scale bioinformatics analysis platform — BGI Online backed by Aliyun (Alibaba Cloud), Intel, and BGI Genomics was launched, which is capable of sequencing the human genome within 24 hours.
Further, Alibaba Cloud is working on a project to train machines to detect lung cancer using high-resolution CT scans.
2. Alphabet — Google
Alphabet is Google’s parent company. Waymo, the company’s self-driving technology division, began as a project at Google. Today, Waymo wants to bring self-driving technology to the world not only to move people around but to reduce the number of crashes. Its autonomous vehicles are currently shuttling riders around California in self-driving taxis. Right now, the company can’t charge fare and a human driver still sits behind the wheel during the pilot program.
Google signaled its commitment to deep learning when it acquired DeepMind. Not only did the system learn how to play 49 different Atari games, but the AlphaGo program was also the first to beat a professional player at the game of Go.
Another AI innovation from Google is Google Duplex. Using natural language processing, an AI voice interface can make phone calls and schedule appointments on your behalf. Learn even more about how Google is incorporating artificial intelligence and machine learning into operations.
DeepMind, Alphabet Inc.’s artificial intelligence research unit, today detailed new machine learning technology it has developed to make Google Maps more useful.
Maps have more than a billion users worldwide who rely on the service to plan their travel routes. One of the service’s most central features is its ability to generate time of arrival estimates, helping drivers view key information such as how soon they need to depart to catch a train.
DeepMind teamed up with sister company Google LLC to reduce inaccuracies in the time of arrival estimates. Their collaboration, the unit detailed this morning, has produced a double-digit reduction in the percentage of inaccuracies. In one case, prediction errors dropped by no less than 51%.
DeepMind achieved this improvement by implementing a so-called “graph” neural network in Maps to help with arrival time estimation. A graph is a data structure that stores data points and the relationships between them in the form of interconnected dots. This structure, DeepMind has found, lends itself well to capturing the interconnected nature of road systems.
But the process wasn’t as simple in the case of Maps because of differences in the way roads are built. An AI that is trained to estimate the duration of highway trips won’t necessarily be capable of doing the same for urban roads, and much smaller differences can cause accuracy issues as well.
DeepMind solved the challenge by taking advantage of its neural network’s graph structure. The unit’s engineers organized the road data that the AI processes to estimate arrival times into “Supersegments” also based on a graph structure, much like the AI itself. These Supersegments are sufficiently flexible that DeepMind’s neural network managed to overcome training data differences.
Different ways Facebook uses Deep Learning to gain value and help Facebook achieve its goals of providing greater convenience to users, and enabling them to learn more about us.
a). Textual Analysis
A large proportion of the data shared on Facebook is still text. Video may involve larger data volumes in terms of megabytes, but in terms of insights, text can still be just as rich. A picture may paint 1,000 words, but if you just want to answer a simple question, you often don’t need 1,000 words. Every bit of data that isn’t essential to answering your question is just noise, and more importantly, a waste of resources to store and analyze.
Facebook uses a tool it developed itself called DeepText to extract meaning from words we post by learning to analyze them contextually. Neural networks analyze the relationship between words to understand how their meaning changes depending on other words around them. Because this is semi-unsupervised learning, the algorithms do not necessarily have reference data — for example, a dictionary — explaining the meaning of every word. Instead, it learns for itself based on how words are used.
This means that it won’t be tripped up by variations in spelling, slang, or idiosyncrasies of language use. In fact, Facebook says the technology is “language agnostic” — due to the way it assigns labels to words, it can easily switch between working across different human languages and apply what it has learned from one to another.
At present the tool is used to direct people towards products they may want to purchase based on conversations they are having — this video gives an example of how it decides whether providing a user with a shopping link is appropriate or not, depending on the context.
b). Facial recognition
Facebook uses a DL application called DeepFace to teach it to recognize people in photos. It says that its most advanced image recognition tool is more successful than humans in recognizing whether two different images are of the same person or not — with DeepFace scoring a 97% success rate compared to humans with 96%.
It’s fair to say that the use of this technology has proven controversial. Privacy campaigners said it went too far as it would allow Facebook — based on a high-resolution photograph of a crowd — to put names to many of the faces which is clearly an obstacle to our freedom to move in public anonymously. EU legislators agreed and persuaded Facebook to remove the functionality from European citizens’ accounts in 2013. Back then the social media giant was using an earlier version of the facial recognition tool which did not use Deep Learning. Facebook has been somewhat quiet about the development of this technology since it first hit headlines, and can be assumed to be waiting on the outcome of pending privacy cases before saying more about their plans to roll it out.
c). Targeted advertising
Facebook uses deep neural networks — the foundation stones of deep learning — to decide which adverts to show to which users. This has always been the cornerstone of its business, but by tasking machines themselves to find out as much as they can about us, and to cluster us together in the most insightful ways when serving us ads, it hopes to maintain a competitive edge against other high-tech competitors such as Google who are fighting for supremacy of the same market.
IBM has been at the forefront of artificial intelligence for years. It’s been more than 20 years since IBM’s Deep Blue computer became the first to conquer a human world chess champion. The company followed up that feat with another man vs. machine competitions, including its Watson computer, winning the game show Jeopardy. The latest artificial intelligence accomplishment for IBM is Project Debater. This AI is a cognitive computing engine that competed against two professional debaters and formulated human-like arguments. IBM artificial intelligence technology is now 95 percent accurate in predicting workers who are planning to leave their jobs.