Artificial Intelligence or AI is the mantra of the current era. This is how Alexa listens when you request her to play your favorite song and responds by playing it, this is how Google ranks pages and shows you the most preferred restaurant you would like to go for lunch, this is how driverless cars detect objects around it and drives accordingly. You can call it magic or simply AI.
AI is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. The modern definition of artificial intelligence (or AI) is "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success.
John McCarthy, who coined the term in 1956, defines it as "the science and engineering of making intelligent machines.” Other names for the field have been proposed, such as computational intelligence, synthetic intelligence or computational rationality.
In simple words, we can say that AI is the ability of a computer program or a machine to think and learn. The concept of AI is to build machines capable of thinking, acting, and learning like humans. Artificial Intelligence can be accomplished by studying how the human brain thinks, and how humans learn, decide and work while they try to solve a problem, then using these outcomes of the study intelligent software and systems are developed.
The main goal of AI is to create expert systems which exhibit intelligent behavior, learn, demonstrate, explain, and advice its users. Also, to implement Human Intelligence in Machines to create systems that understand, think, learn, and behave like humans.
We are all aware of some of the applications of AI which we encounter in our day to day lives such as -
Vision systems - These systems understand, interpret, and comprehend visual input on the computer. For example, whenever we run a red light or stop signal in a car, a fine or ticket is raised against our license plate number. This is one of the cases where the cameras in the streets capture frames and detect when a vehicle crosses a red light or stop signal, and records the specific license number. Similarly, police use these systems to recognize the face of criminal with the stored portrait made by forensic artist.
Speech Recognition − There are intelligent systems which are capable of hearing and comprehending the language in the form of sentences and their meaning when human talks to them. It handles all different accents, slang words, change in human’s voice due to cold and so on. A very common example is voice assistants such as Alexa by Amazon, Siri by Apple, or Cortana by Microsoft. These voice assistants recognize words/phrases/sentences and respond according. If you simply call Alexa by her name, she responses, and waits for a command, then you may ask her to perform an action or simply ask to play music or ask a question.
The core problems that are associated with AI include programming computers with certain traits such as:
1943 - Alan Turing invented the "Turing Test", which set the bar for an intelligent machine: a computer that could fool someone into thinking they were talking to a real person. Grey Walter built some of the first ever robots.
1950 - Early AI research was more of exploring topics like problem-solving and symbolic methods. I, Robot was published - a collection of short stories by science fiction writer Isaac Asimov.
1956 - John McCarthy coined the term "artificial intelligence." A "top-down approach" was dominant at the time: pre-programming a computer with the rules that govern human behavior.
1960 - During this time, the US Department of Defense gained interest in this kind of work and started training computers to mimic basic human reasoning.
1968 - Marvin Minsky, the founder of AI Laboratory at MIT, advised Stanley Kubrick on the film 2001: A Space Odyssey, featuring an intelligent computer, HAL 9000.
1969 - Shakey the Robot, the first general-purpose mobile robot was built. It was able to make decisions about its own actions by reasoning about its surroundings.
1970 - DARPA or Defense Advanced Research Projects Agency completed the street mapping project.
1974 - The "AI WINTER" began - millions had been spent, with little show for it. As a result, funding for the industry was slashed.
1980 - During this period, a form of AI program called "expert systems" was adopted by corporations around the world and knowledge became the focus of mainstream AI research. The period 1980-1987 is termed as “Boom”.
1990 - This period Artificial Intelligence experienced a major financial setback. Researchers termed this period (1987-1993) as “Bust”.
1997 - Deep Blue became the first AI enabled the computer to beat chess against world champion Garry Kasparov.
2003 - DARPA had already produced an intelligent personal assistant long before Apple’s Siri, Alexa or Cortana came into the picture.
2008 - Google launched a speech recognition app on the new iPhone. It was the first step towards Apple's Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana.
2011 - In this year, IBM Watson beat the top two Jeopardy! players Brad Rutter and Ken Jennings.
In 2018, Artificial Intelligent is not a buzzword anymore. World's first AI presenter was unveiled in China. China’s Xinhua state news agency had introduced the newest members of its newsroom, AI anchors who will report 'tirelessly' all day, every day, from anywhere in the country.
On February 24, 2019, China’s Xinhua introduced the world’s first female AI news anchor, who will debut in March.
The field of AI, now after over half a century, has finally achieved some of its goals. It is being used throughout the technology industry very successfully and also in other industries as well. All of these were achieved due to the increase in computer power and because researchers and professionals focussed on specific isolated problems.
We all have been seeing how AI has changed the way we think and interact with each other every day. It is true that AI was merely a science fiction and now has turned into reality. The statistics related to AI in the field of business and technology are also changing. In all sectors, whether it is healthcare, education or manufacturing, there is a success in nearly every industry as they adopt artificial intelligence.
With the effect of AI on robotics, virtual digital assistants, voice search and recognition, startups and investments, big data, there has been a change in the statistics and has new goals with respect to AI.
According to research and surveys, here are some of the statistics related to Artificial Intelligence.
“Accenture researched on the impact of AI in 12 developed countries and revealed that AI can double the economic growth rates in 2035. It can be achieved by changing the nature of work and creating new relationships between machine and man. AI’s impact on businesses will enable people to use time efficiently and increase their productivity by 40 percent.
MIT Sloan Management Review published an article which shows that 75 percent of executives believe that AI will enable their company or enterprises to expand and gain a competitive advantage.
Adobe surveyed almost 500 marketing and IT professionals to explore current mobile trends, forecast where mobility is going, and learn what some of the most advanced organizations are doing in the space. It has been seen that almost 47% of the advanced enterprises have applied AI strategies to their mobile applications as part of their marketing efforts and additionally, 84% use a personal strategy.
This information clearly shows that people are slowly increasing the use of voice search in everyday lives.
Audio-centric technologies like Amazon Echo have access to dialogue-based information. According to the AI statistics provided by Gartner, the voice-first interaction will gain prominence in no time.
A press release by IHS Markit, a business information provider, found that 4 billion devices have AI-powered assistants, and this number will reach 7 billion by 2020.
A 2017 Pew Research study showed that 46% of Americans use digital assistants to interact with their smartphones. Voice assistants are present on a diverse range of devices, so 42% of users have the tech on their smartphones, 14% of them use it on a computer or tablet, while 8% of them use it on a standalone device such as Amazon Echo or Google Home.
The general benefit of artificial intelligence, or AI, is that it replicates the decisions and actions of humans without human shortcomings, such as fatigue, emotion and limited time. Apart from this, there are a few more benefits which are mentioned below -
Concerns about disruptive technologies are common. Automobiles are one of the examples, it took almost a decade to develop regulations around the industry to make it safe. Today AI has been highly beneficial to society as it enhances efficiency and throughput, creating new opportunities for revenue generation and job creation.
Humans are not very good with tedious tasks but machines are. AI allows humans to do the more interpersonal and creative aspects of work.
It is said that Robots and AI will destroy jobs. This is fiction rather than fact. People will still work, have their jobs, but they will work better with the help of AI
Introducing AI in our society will enhance our lifestyle and create more efficient businesses. Some of the mundane tasks like answering emails and data entry will be done by intelligent assistants. Society will switch to smart homes in order to reduce energy usage and provide better security, marketing will be more targeted and we will receive better healthcare.
AI can be used to perform tasks which would once require intensive human labor or would not have been possible at all. Also, due to AI automation, there has been a reduction in operational costs which is a major benefit for businesses.
Computers definitely do not share the same probability of errors as human beings are. AI can be used to analyze and research historical data to determine the efficiency of distributing energy loads from a grid perspective.
AI has been playing an important role in multiple industries like health sciences, academic research or technology applications where a lot of AI-based applications are in use, such as character/facial recognition, digital content analysis and accuracy in identifying patterns and so on.
AI has been a boon to mankind. It has been the biggest opportunity of our lifetime to extend and expand human creativity and ingenuity.
There are many amazing ways in which artificial intelligence and machine learning are being used to impact our everyday lives. Also, it has been implemented in the world’s leading companies to simplify business decisions and optimize operations. Let us walk through some of the practical examples of AI and machine learning.
Hello Barbie listens and responds to a child using natural language processing, machine learning, and advanced analytics. A microphone is attached to the Barbie’s necklace which records what is said and transmits it to the ToyTalk servers. Then, the recording is analyzed to determine the appropriate response from 8,000 lines of dialogue. Toytalk Servers transmit the correct response back to Barbie in under a second so she can respond to the child. Some of the answers are stored in the form of dialogues such as Barbie’s favorite food etc.
Coca-Cola’s global market has more than 500 drink brands sold in more than 200 countries. It makes it the largest beverage company in the world. The company generates a lot of data and it has embraced new technology to put that data into practice in order to support new product development and even trialing augmented reality in bottling plants.
Culinary arts do require the human touch. But AI-enabled Chef Watson from IBM has changed the notion. It uses artificial intelligence to become a sous-chef in the kitchen and helps in developing recipes. Chef Watson also advises their human counterparts to create delicious and unique flavors.
IBM has come up with Watson BEAT, which has the ability to deliver different musical elements to inspire music composers. Such AI-based products help musicians and composers understand the requirement of the audience and also figure out what kind of songs might be a hit number.
To deliver energy into the 21st century, big data, machine learning and Internet of Things (IoT) technology is being used by GE Power in order to build an internet of energy. Advanced predictive analysis is also being used to predict the maintenance and to optimize the operations and business.
American Express processes $1 trillion in transactions and has 110 million AmEx cards in operation. To process such a heavy number of transactions, AmEx is highly dependent on data analytics and machine learning algorithms. It also uses Big Data analytics to detect more fraudulent transactions and save millions.
The foundation for Google’s DeepMind has always been Neuroscience. It creates a machine which has the ability to mimic the thought process of our own brains. DeepMind has been proven successful by beating humans at games but now it is time to use the same for healthcare purpose which might reduce the time to plan treatments and also help diagnose.
Automobiles generate a lot of data that can be useful in various ways. Volvo is among one of the vehicle manufacturing companies which uses data to predict engine failure or when vehicles need servicing and thereby expands its services in monitoring vehicle performance. This indeed improves both driver and passenger convenience and safety.
Recommendations are what help grow businesses. Netflix is using big data analytics to predict what will its customers prefer to watch. They are not only a media distributor but also a content creator. Analyzing and predicting data helps them to decide what content they should invest in.
Burberry is a luxury fashion brand but generally, we would never consider it to be a digital business. But they have been reinventing themselves with the help of AI and Big Data. It has improved its sales and customer relationship.
Instagram is said to be the most visited social media by the youth. It generates a lot of data in the form of images, videos, and comments. Some of these are offensive and therefore Instagram uses big data and artificial intelligence to fight cyberbullying and delete offensive comments. Apart from these, it also uses deep learning algorithms to detect the type of images and suggest filters for the same.
to innovation labs. However, every business needs to overcome challenges to understand the true potential and possibilities of this emerging technology.
Some of the organizations involved in AI are unable to demonstrate clearly what AI does. No wonder AI is a “black box”. This results in people being skeptical about it as they fail to understand the logic behind it or how it makes decisions. AI needs to explainable, provable and transparent. It will be a good practice if organizations using AI embrace Explainable AI.
Most AI applications depend on huge volumes of data to learn and make intelligent decisions. Generally, Machine Learning is largely dependent on data and often this data is sensitive or personal in nature. This makes the system vulnerable and leads to serious issues such as data breach and identity theft. Due to the increasing number of such cases, consumers have prompted the European Union (EU) to implement the General Data Protection Regulation (GDPR), which ensures the protection of personal data. Apparently, it will empower Data Scientists to develop AI without compromising on consumers’ data security.
Today, organizations have access to more data than ever before. However, AI applications require relevant datasets to learn, but datasets are rare in number. The most powerful AI applications are the ones which are trained on supervised learning i.e with the help of labeled data but again, labeled data is limited. It is necessary for the organizations to invest in design methodologies and figure out the possible ways to make AI models learn despite the scarcity of labeled data.
Artificial Intelligence works by combining large amounts of data and processes fast using intelligent algorithms and then allows the software to learn automatically from patterns or feature in the data. Artificial Intelligence, being vast and a broad field of study, includes theories, methods, and technologies as well as the following major subfields:
Additionally, several technologies enable and support AI:
The three levels of AI are ANI, AGI, and ASI. Narrow, strong, and super artificial intelligence.
Example: RankBrain by Google and Siri by Apple
Artificial Intelligence that is focussed on one narrow task is called as Narrow AI or Weak AI. In this case, the ability of an AI application or machine to mimic human intelligence and/or behavior is isolated to a narrow range of parameters and contexts.
We need to keep in mind that we are talking about narrow intelligence, not low intelligence.
Siri is a perfect example of Narrow AI. Also, most of the application of AI we see in our day to day lives falls under Narrow AI.
The intelligence of a machine that could successfully perform any intellectual task that a human being can be called as Strong AI or Deep AI. In this case, the ability of an AI application or machine to mimic human intelligence and/or behavior is indistinguishable from that of a human.
A hypothetical AI replicating a human baby would be an example of strong AI while being "weak" at most tasks.
Artificial superintelligence (ASI) is a software-based system with intellectual powers beyond those of humans across an almost comprehensive range of categories and fields of endeavor. In this case, an AI application or machine doesn't mimic human intelligence and/or behavior but surpasses.
Unlike weak and strong AI, Artificial Superintelligence (ASI) is something that researchers are not yet confident about. We can only speculate about it. It should have the ability to surpass all human activities at all things, whether it is writing books, solving a mathematical equation or prescribing medicines.
But it is a big question for the AI enthusiasts, whether ASI is possible.
If we consider that ASI would be possible then it should have the capability to do things we believe humans can do better than bots, such as relationships and the arts. Experts believe that not only ASI but even AGI requires decades more research.
Determining what humans or other entities such as robots are doing. For example, a car that can see its owner approaching with a heavy bag of groceries may decide to open an appropriate door automatically.
AI that seeks reading and using emotion.
Artificial intelligence, science, and engineering modeled upon living systems. It has three types known as soft, hard and wet for software, robotics, and biochemistry respectively. The term wet refers to the water content of living systems.
Automation of decisions or physical tasks using machinery such as robots.
A program that simulates intelligence by retrieving information from a data repository. In some cases, products claim to be artificially intelligent as a marketing approach when their software has a design that doesn't learn in a dynamic way.
Artificial intelligence that can talk to humans, often over text chat. Typically designed to pass a Turing test.
Analyzing and understanding visual information is a reasonably complex task that typically requires artificial intelligence.
The use of artificial intelligence to support human decision making. For example, a tool that determines what information you may need in a given situation.
A machine learning technique that uses multiple learning algorithms.
Machine Learning algorithms learn from historical data and allow computers to find hidden insights and pattern without being explicitly programmed.
Machine Learning can be categorized into two main categories:
The ability to recognize, interpret and synthesize speech.
Artificial neural networks are an AI approach that was originally inspired by biological neural networks. With time their resemblance to biology has decreased and they are now typically based on statistics and signal processing techniques.
Tools that determine the general opinion, emotion or attitude in content such as a comment in social media.
Consider three Russian dolls (Matryoshka dolls), out of which the largest one is Artificial Intelligence (AI), within it is Machine Learning and within that is Deep Learning.
All Machine Learning is Artificial Intelligence but not all Artificial Intelligence is Machine Learning.
|Cognitive AI||Machine Learning||Deep Learning|
|Capabilities||Simulates human thought processes finds patterns in data to assist humans to find solutions to complex problems||Finds patterns in data using advanced analytical approach and model building||Leverages pattern-matching techniques to analyze vast quantities of unsupervised data|
|Purpose||Augment human capabilities and automate processes||Provide systems the ability to automatically learn and improve from experience without being explicitly programmed||Enables machines to process data with a nonlinear approach.|
|Industries||Transportation, Healthcare, Finance, Manufacturing industries, Retails, Advertising, Agriculture, Automobiles, Aerospace, Genomics, Pharmaceutical, Cybersecurity,||Transportation, Healthcare, Finance, Manufacturing industries, Advertising, Agriculture, Automobiles, Aerospace, Logistics||Genomics, Pharmaceutical, Cybersecurity, Agriculture, Automobiles, Aerospace, Logistics|
First release: 1995, latest release: 2014
Java is an object-oriented programming language that follows the principle of WORA (“write once, read everywhere”). It runs on all platforms without any additional recompilation due to Virtual Machine Technology. Some more advantages of Java is that this language is easy to use and easy to debug. However, in term of speed, it loses against C++. Java AI programming is a good solution for neural networks, NLP and search algorithms.
Initial release: 2001, latest release: 2011
Extended from: XML
AIML (Artificial Intelligence Markup Language) is a dialect of XML used to create chatbots. Due to AIML, one can create conversation partners speaking a natural language.
The language has categories showing Which primary programming languages can be used for AI?
Python is an interpreted, high-level, general-purpose programming language.
C++ is one of the fastest programming languages in the world and it is a major advantage for AI.
Lisp, being the second oldest programming language in the world (after Fortran), still holds a top position in AI creating due to its unique features.
The name of Prolog speaks for itself; it’s one of the oldest logic programming languages. If we compare it with other languages, we can see it is declarative. It means that the logic of any program will be represented by rules and facts. Prolog programming for artificial intelligence can create expert systems and solving logic problems. Some scholars claim that an average AI developer is bilingual – they code both Lisp and Prolog.
Homepaa unit of knowledge; patterns of possible utterance addressed to a chatbot, and templates of possible answers.
One of the predictions by Gartner said -
“By the end of 2018, “customer digital assistants” will recognize customers by face and voice across channels and partners.
Multichannel customer experience will take a big leap forward with seamless, two-way engagement between customer digital assistants and customers in an experience that will mimic human conversations, with both listening and speaking, a sense of history, in-the-moment context, tone, and the ability to respond.”
We have already seen in mid-2018, Google coming up with a smarter voice assistant.
AI is not just limited to IT or technology industry it is widely being used in other areas such as medical, business, education, law, and manufacturing.
In the following, we have a few intelligent AI solutions that we are using today:
We all know about Apple’s voice assistant, Siri, it uses machine-learning technology in order to get smarter and capable-to-understand natural language questions and requests. It is one of the most iconic examples of machine learning abilities of gadgets.
Not just smartphones, but automobiles are getting smarter as well as they shift towards Artificial Intelligence. Tesla is one such example in the automobile industry. It has features like self-driving, predictive capabilities and so on. Tesla is getting smarter day by day through over the air updates.
This company is a synthesis of machine learning and behavioral science to enhance customer collaboration for phone professionals. It is applicable to millions of voice calls that take place on a daily basis. The AI solution provides real-time guidance by analyzing the human voice.
Netflix is a popular content-on-demand service which uses predictive technology to recommend its consumers’ with respect to their interests, choices, and behavior. It is getting intelligent day by day.
Nest, being on the most successful AI startups was acquired by Google in 2014. Nest Learning Thermostat makes the use of behavioral algorithms to save energy based on your behavior and schedule. It takes about a week to program itself and then learns the temperature you like. If nobody is at home, it tends to turn off automatically to save energy.
Amazon Echo, helps you search for information on the web, schedule appointments, control household equipment, acts as a thermostat, answers to questions, reads audio books, update you about traffic and weather, gives you info on local businesses. All of these just by calling out “Alexa” (Amazon’s voice service). It is getting smarter and adding new features.
AI is being adopted by organizations rapidly. It has become more important than ever to know the options AI offers in terms of tools, libraries, platforms and so on. Here we have mentioned a few of the platforms that support AI.
Azure Machine Learning is a cloud-based service that provides tooling for deploying predictive models as analytic solutions. Apart from these, it can also be used to test machine learning models, run algorithms, and create recommender systems. For people lacking advanced programming skills and would like to get into machine learning should check this out.
Amazon Web Services has the broadest and deepest set of machine learning and AI services. Pre-trained AI services are available for computer vision, recommendations, forecasting, etc.. You can also use Amazon SageMaker to build a model quickly and then train and deploy machine learning models using all popular open-source frameworks.
With enterprise AI on the rise, speed and agility are crucial to keeping competitive, yet custom solutions can be time-consuming, complex, and costly. With Google Cloud AI solutions, you can quickly and easily apply solutions across your work streams or combine our technology with vendors you already work with. Whether you’re looking to classify images and videos automatically or deliver recommendations based on user data, you can use Google Cloud AI Solutions to drive insights and improve customer experiences.
During 2018, there was a rise in the platforms, tools, and applications based on Machine Learning and AI. These technologies had a good impact not only with the software and internet industry but also other industries like healthcare, manufacturing, automobile and so on.
Unlike software, AI is heavily dependent on specialized processors that complement the CPU. In 2019, Intel, NVIDIA, AMD, ARM, Qualcomm, and other major chip manufacturers will manufacture specialized chips that will speed up the execution of AI-enabled applications. These chips will be optimized to perform computer vision application, natural language processing, and speech recognition.
Artificial Intelligence will meet IoT at the edge computing layer in 2019. Industrial IoT which can be considered as the top use case for artificial intelligence, can perform outlier detection, root cause analysis and predictive maintenance of the equipment. Most of the models which are trained in the public cloud will be deployed at the edge.
IoT will eventually become the biggest driver of artificial intelligence in the enterprise. We will get to see edge devices getting equipped with the special AI chips based on FPGAs and ASICs.
Choosing the right framework while developing neural network models has always been a critical challenge. Developers and data scientists need to pick the right tool from a bucket full of choices which include Caffe2, PyTorch, Apache MXNet, Microsoft Cognitive Toolkit, and TensorFlow. It is tough to port a model, which is already trained and evaluated in a specific framework, to another framework. Basically, there is a lack of interoperability among neural network toolkits.
Microsoft, Facebook, AWS have collaborated with each other to build Open Neural Network Exchange (ONNX). It helps to reuse trained neural network models across multiple frameworks.
AutoML is going to change the face of ML-based solutions. Business analysts and developers will be empowered to evolve machine learning models which will be able to address complex scenarios without going through the typical process of training ML models.
OccamzRazor is mapping the human Parkinsome — a dynamic knowledge map that reveals the hidden mechanisms and new treatments of Parkinson’s Disease.
Umbo Computer Vision is an artificial intelligence company building autonomous video security systems for businesses and organizations.
Gamaya addresses the need to increase efficiency and sustainability of large industrial farming, as well as the productivity and scalability of smallholder farming, by deploying the world’s most advanced solution for mapping and diagnostics of farmland.
Spatial.ai is a location data company that uses conversations from social networks to understand how humans move and experience the world around them.
Textio is the augmented writing platform that tells you who will respond to your writing based on the language you have included in it and gives you guidance to improve it.
There are two things that reveal how well a particular country is positioned to leverage the development pipeline.
First one is the pool of available talent. Qualified professionals are necessary for any country to push AI forward. Some of the countries have also developed university programs on AI curriculum to develop more talent. Intellectual capital is a huge advantage when it comes to emerging technologies.
The second one is the level of AI and digital activity that take place in the country. This also includes the amount of funding in circulation. All these countries are building the foundation to support the future of AI.
Keeping these criteria in mind, the following countries are ahead in the race to rule the world with the help of AI:
United States: The United States is leading with $10 billion in venture capital which has been funneled to AI. According to a report, there are almost 850,000 AI professionals in the United States which is definitely more than any other country. The top players - Google, Facebook, Amazon, Microsoft are investing heavily in Artificial Intelligence and the United States will soon have every resource necessary to become a global leader in automation.
China: It is essential for China to push forward with AI in order to maintain the country’s economic growth. They have set aggressive targets for 2030. In a period of 5 years, there was growth by 190% in the number of patents that were granted, the effects of automation have been remarkably significant. According to estimates, AI could increase the economic growth of China by 1.6% by 2035.
Japan: Japan can be called as the historic leader in robotics. Due to the unique feature of the economy of Japan, it can absorb a greater amount of automation than other countries. A study has rated the automation potential of the manufacturing sector of Japan at 71%, compared to the United States’ which is at 60%.
Russia: By 2025, Russia’s intention is to turn 30% of the country’s military equipment robotic. Machine Learning and algorithms have already been leveraged by the country’s intelligence to project pro-Russia messaging into foreign media markets. Russia’s enthusiasm for AI has always been up and high.
Estonia: Estonia is another country which has been manufacturing intelligent machines. According to Akamai’s 2017 report, this country ranks 27th in the world for the fastest internet and beats the United States as well. The country also has the third most startups per capita in Europe which leads to a lot of innovation and fundraising to support AI.
In the following we have mentioned some of the recent developments in AI, which demonstrates how technology is advancing:
In most of the smartphone apps which are designed for everyday consumers, you will find that AI is making an appearance. According to Gartner, by 2022, on-device AI capabilities will rise from 10% to 80%. This will also give an opportunity to the developers to deploy AI in all types of apps. Here are AI is currently being used:
FinTech is another area where we have seen a lot of disruptive technology in the last decade. Artificial Intelligence is another disruptor in this particular sector. Artificial Intelligence has been able to reduce processing time.
Chatbots are being used by the bank to replace the traditional customer service suite. There have been apps developed to connect financial accounts with Facebook Messenger (for example Trim) allowing customers to ask questions or place a complaint, make transactions or get reports via the app.
Fraud Detection is a crucial process in this sector. AI-based Applications like Pixmettle has developed enterprise level AI tools to help flag things like duplicate expenses and corporate policy violations.
As the use of technology increases, the potential threats to sensitive information also increases. There has been a demand for AI solutions to boost cybersecurity. It is expected by professionals that AI-based cybersecurity will accelerate incident detection, improve incident response, identify and communicate risk and also maintain optimum situational awareness.
Google’s parent company. Alphabet had introduced Chronicle, which is basically a cybersecurity intelligence platform. It is a powerhouse for cybersecurity data and allows rapid search and discovery.
Artificial Intelligence “learns” when humans or machine learning trains and a bot learns by processing data. For example, if you tend to go to the same place every day morning for coffee, the bot might learn the trend and automatically start to look for traffic or weather conditions and provide you with an estimated driving time daily.
NVIDIA demonstrated a robot which can perform tasks in a real-world setting by watching how the tasks are done. The robot has the ability to learn through observing the actions of humans.
Along similar lines, a bot program called Alpha Go learnt itself advanced strategies to play the game GO without any training from humans. This also highlights the fact that AI is able to be independent of human knowledge.learned
In medical technology, AI has been very effective in areas such as diagnostics and so on. Certain cases require a human operator to be able to read and interpret tests or imaging results but AI-based medical technology has left with lesser involvement and has also reduced human error.
In a recent development, machine learning was used to do machine learning, computer-generated x-rays were used to augment AI training.
“We are creating simulated x-rays that reflect certain rare conditions so that we can combine them with real x-rays to have a sufficiently large database to train the neural networks to identify these conditions in other x-rays.” – Shahrokh Valaee