Business perspective in the Future of AI development by 2025
IT Project Manager, working in a Data Science Team over the NLP, Computer Vision and Fraud Detection
“Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing. We're nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on.” —Larry Page (CEO of Alphabet)
Table of content
As far as the impact of AI and Machine Learning on the modern world is constantly growing and changing every industry - from Education to Robotics and Heavy machinery, there is no doubt that tomorrow will be revolutionized by these two areas of computer science. Many cutting-edge inventions have already been made so far: Personal Assistants with a high level of human language understanding as Siri and Alexa, self-driving Tesla cars, IBM’s Watson
(won against 2 best performers at the Jeopardy show), autonomous weapons, dancing NAO Robots (2010 World Expo) and more.
Numbers talk louder then simple words, so let’s take a look at some predicted statistics of AI in business for next 5 years:
- By 2025 the whole AI market that was only $1.6 billion 4 years ago, will be around $60 billion and the world’s GDP will rise by $15 trillion before 2030 because of AI.
- The intensively rising number of AI startups has been noticed since last two decades - at about 14 times more, than it was, and equivalently the amount of investments in this area also grew almost 6 times.
- Data Scientists from Google assume that only in the next year(2020) robots will manage to imitate complicated human behaviour and emotions like flirting or making fun of somebody.
- Large businesses, with more than a hundred of employees are likely to have a development course that will somehow be related to AI.
- Almost 50% of the enterprises have a fixed roadmap for Mobile that involves AI in it.
- 85% of Global corporations are persuaded that AI will give them an advantage among their rivals.
AI software development top-notch companies
Commonly recognized AI software development services which offer generalized cloud solutions for various purposes and can be hired at any time, such as Google and AWS dictate the new 2019 trends for AI and Machine Learning. They ignite the attention of people, greatly interested in innovative technologies, by their Machine Learning products such as AWS
and Google AI
Nevertheless AWS and Google AI are on the top-list to build our AI future, a leader in winning the AI race is apparently Deepmind, a British company founded in 2010 in London. It has been conducting much medical research with AI, such as examining the protein with AlphaFold
system, predicting eye disease and helping doctors in identifying life-threatening illnesses.
Along with the well-known AI giants, our AI Development company SPD.GROUP
also tackles various issues in Finance&Bankig, CyberSecurity, E-Commerce and other industries with AI driven models. We have been working on Anomaly Detection in Online Transactions
, Recommendation system, Customer Service tools to help our business clients get insights and formulate a better market strategy or make their platforms a more safe and reliable place for customers.
Benefits of AI in different industries
Targeted advertising, Market analysis, Client segmentation
AI here helps renters and buyers who are searching for in a property connect with roommates and partners to collaborate. Another case is when an algorithm on a real estate site helps to buy or rent a new house taking into account their credit score and the attributes of the property they want to buy. You can put in your credentials (credit score, income, guarantor information and extra savings), and when the algorithm processes this information, it will give you the estimation of your chance to be approved.
Cashless stores, Virtual mirrors, Footfall analysis and store optimization
Robots like Pepper who can perceive human emotions and thus interact with customers are popular in Japan. This robot performs as a customer service greeter and representative in 140 mobile stores of the USA. After the robots appeared at the stores, customers’ interactions almost doubled and the revenue was increased by 300%.
Risk identification, Personalized pricing, Client support
Every major insurance decision will have Artificial Intelligence behind. The AI algorithm analyses vast amounts of data that come from several sources to make a fraud score basing on each customer’s claim and refer an average hit rate that is usually higher than any other automated fraud detection system might have.
Insurance products that are linked to an individual’s activities will generate enormously big data streams and will be processed by AI algorithms. After, they will create risk profiles to reduce time needed for purchasing a carl or a life policy to just a few minutes.
Banking & Personal Finance
Fraud prevention as AI learns what types of transactions are fraudulent, credit decisions, client segmentation
One of the possible AI use cases here is a personal chatbot that responds to voice and text, make payments instead of the customer, monitors savings, understands and addresses customer queries by means of Natural Language Processing. One of such was made in 2015 and named Ally Bank. Machine Learning can be used to spot suspicious data points and show banks if the actual money source is legal or not. AI will determine market trends and make predictions for investors to determine the amount of money they should invest every month to achieve their aims.
Incident detection, accelerated incident response
Among the best use cases of AI for cybersecurity are malware detection, fraud detection, scoring risk in a network, intrusion detection and machine behavioral analysis. AI understands and learns regular user’s behaviour and can detect the smallest deviation from that behaviour. But even more than that, AI can use data about behavioral patterns to improve its own functions and strategies.
To get ready for the hacker attacks, AI algorithms can collect data such as behavioral deviations which are usually typical for hackers: the way a password is entered or where the user has logged in. Detecting these trails, AI can stop a hacker on his or her track.
Reducing travel time by analysing traffic, Ride-sharing apps - determining the price, supply chain prediction, Autonomous vehicles
There are already autonomous taxis driving through Tokyo, and the amount of such vehicles will be rising by leaps and bounds in future. For traffic management - AI will reduce unwanted congestion, improve road safety and wait times. Autonomous car passengers will have an exceptional journey experience with Data Lake techniques and Computer Vision.
The main two ways of classifying AI is to divide it into “Narrow AI” and “Strong AI”, although there is division that is more instructive:
- Reactive Machines - these are machines such as Deep Blue. It can identify pieces of the chess board and make predictions, but it has no memory and cannot use historical data to predict the outcome with the new information.
- Limited Memory machines - these are autonomous vehicles that can use historical data (past experiences) to make decisions on new data.
- Theory of mind - exists as a concept, but no such AI has been invented yet. It implies that a machine would understand that others have their own beliefs and intentions that directly influence the decisions they make.
- Self-awareness - also a non-existing type of AI yet, machines that understand their current state and can use the information to infer about other people’s feelings.
Human’s cognitive system follows three distinct phases in its work.
Observation. When you give the “intelligent” neural network an amount of information huge enough, it starts seeking out patterns and sequences to understand the nature and relationships between the components.
Evaluation. After the model has found some common patterns, it filters these similarities to understand how it should classify them.
Making a decision. Finally, it makes a decision to where new information should be related.
Taking this into account, an AI-based system can actually be named a cognitive system while its work resembles the same phases. Unlike conventional approaches in computing which only handle neatly organized structured data, AI based system can comprehend unstructured data, which is actually most of the data we have today. Everything starting from literature, articles, researcher posts, blogs, posts, tweets. This becomes possible due to the Natural Language Processing which is guided by rules of grammar, context and culture. All the different AI powered opportunities that enhance our work and help us derive significantly more from what we do, include: spell check, autocomplete, voice text messaging, spam filters, related keywords on search engine and so on.
AI Development services for business in the future
he driving power of most businesses is first of all based on understanding of human language by a machine and giving a relevant response to it. This becomes possible with NLP or Natural Language Processing that helps to spell check, autocomplete and autocorrect texts. Among more advanced tasks that AI can do to human language is monitoring and analyzing feedbacks, reviews, support tickets and other communication forms that belong to the tedious and routine part of every business. AI algorithm is able to derive meaning from what is written or said, interpret it and even generate a proper reply. It understands the sentiment and a hidden implication in the text, so it is able to trace what kind of emotions are expressed: sorrow, happiness, rejection or displeasure.
Communication tools advanced by NLP are especially useful for E-commerce platforms and salespeople. Such a popular CRM platform company as Salesforce widely uses NLP so that users do not waste time on checking the grammar or orthography as long as error-free messages to clients are the key to smooth and effective communication.
Search autocomplete is another NLP-based useful feature that such technological pioneers as Google are actively using in their search engines. Users get answers to their questions much faster with only inserting one related word, while the search engine would find a proper match for it and give a list of variants.
Another way how AI can use human language are Smart Messengers, Virtual Assistants and Knowledge Base Support. Smart Bots are tools used by businesses to connect to customers through social media. One of the most known is Facebook Messenger. These Smart Messengers are not simply for advertising items or services, they can provide a customer with a unique experience of interaction. Facebook Messenger was firstly used by Uber in 2015 and it enabled customers to order a car quickly and easily right in the application.
Personal Assistants can be used just as a bank teller which is always at hand and knows everything about your budget. Such an example of “bank teller” was launched by Mastercard in 2016. It helps to eliminating the need in usage of a separate app and is a beneficial thing for Mastercard company, not to develop one more application dedicated to these functions precisely.
AI will be widely used to investigate customers’ experience with brands, preform social media monitoring and improve content marketing strategy, automatically derive insights. Public opinion about a brand is discovered while extracting the information from surveys and analyzing the data for keyword frequency and trends. An example of such might be IBM SPSS Text Analytics for Surveys.
While content writers generate articles, software powered by NLP and AI will analyze the text and give detailed directions for writers to achieve the highest quality.
The next step would be automatic insights generation, meaning that the business tool does not only analyze a collection of texts, but also can formulate insights in forms of sentences out of it, so that you would instantly have a summary(an idea) of what the text is about.
Most common AI use cases in the Future
The ultimate goal for AI in Transportation is to make it automated in the future. It will bring along such benefits of AI as :
- decreasing the number of car accidents caused by drivers or weather conditions;
- reducing aggressive driving on the roads;
- decreasing the time needed to get somewhere and boosting travel time reliability;
- enabling disabled and older users to drive a car;
- increasing the effectiveness of current transportation systems.
Dangerous jobs are run by robots
Instead of taking over the human jobs, which is a major threat for many people, robots will reveal people from tedious, dull and dangerous pieces of work:
- robotic welding (eliminates the likelihood of serious illnesses due to fumes from metals);
- robots are sent to unreachable places in a burning house to help the firefighters monitor the progression of fires and the damage caused;
- a robotic cowboy to keep farm workers safer;
- highly dangerous operations can be conducted remotely with robotic devices;
- rescuing disasters victims with robots;
- robots for Pipeline and Fuel tank inspection.
Predicting the future
As long as Machine Learning uses past events to make predictions about the future, it will be able to predict even the most intimate things such as who will start dating or who will get divorced.
- AI is able to predict premature death(using health data on around half a million people, demographic data and lifestyle choices);
- Predict ROI of a business;
- AI in all industries can predict when something breaks down or runs out of control;
- Make predictions for supply chain and logistics to save time and costs, produce less waste.
While changing every single industry in the world, AI is making business faster, smarter and more secure. With Machine Learning, businesses have a personalized customer experience, can speed up they content marketing strategy and improve the overall business strategy deriving trends or insights from a huge amount of feedbacks, articles, news from the Internet. Coming to replace the artifacts of the past such as Tabulating machines and conventional computing solutions which were first simple machine solutions, the AI applications are now mimicking human brain and evaluating tons of information to give the user a reasonable output.
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