What is artificial intelligence and how can it already be used in companies?
Artificial intelligence (AI) is the mechanical simulation of processes of human intelligence. These processes include learning (acquiring information and rules for using information), reasoning (using rules to reach approximate or final conclusions) and self-correction.
A strong AI system can find a solution without human intervention when confronted with an unknown task. Since the hardware, software and personnel costs of AI can be expensive, many vendors integrate AI components into their standard offerings and access to Artificial Intelligence as a Service (AIaaS) platforms. This allows individuals and businesses to experiment with AI for different business purposes and test multiple platforms before starting a business. Amazon AI, IBM Watson Assistant, Microsoft Cognitive Services and Google AI Services are popular cloud offerings in this segment.
The highest adoption rates in AI are found in industries such as high tech, telecommunications and automotive, and these are also the most digitized. Similarly, AI’s early adopters have already invested in digital infrastructure, including cloud and big data. In fact, without the experience of digital transformation and a minimum of digital infrastructure, companies cannot simply use AI.
In the field of artificial intelligence, there are different characteristics and areas of application. Basically, there are two types of AIs, in order to classify them better.
Weak AIs concentrate on very limited areas of responsibility. An example would be a poker game where all the rules and moves are entered into the machine by a machine that beats people. Each possible scenario must be entered manually beforehand.
Strong AI: :
Machines that, just like a human being, can actually think for themselves and perform tasks. A lot of research is already being done in this area, but there is still no AI that comes close to the intelligence of a human being.
This is one of the basic forms of AI. It has no past memory and cannot use past information for information for future actions. Example: IBM chess program in the 90s that defeated Garry Kasparov.
Limited storage capacity:
AI systems will be able to use past experience to make future decisions. This was developed to develop some of the decision-making functions in self-propelled cars, such as observations that served to support actions in the not too distant future, such as a lane change. These observations are not stored permanently.
Theory of mind:
This type of AI should be able to understand the emotions, beliefs, thoughts, expectations of people and interact socially. Although much progress has already been made in this area, research is still in its infancy.
An AI with own consciousness, high intelligence, ability to learn and feelings, thus equal to a human being. This level has not yet been reached by any AI and would be a milestone in human history.
What AI technologies are available?
Machine Learning (ML):
A method in which the goal is defined and the steps to achieve this goal are learned by the machine itself through training (gaining experience). For example, to identify a simple object such as an apple or an orange. The goal is not achieved by explicitly specifying and encoding the details, but as we teach a child by showing several different images of it, thus allowing the machine to define the steps to identify it as an apple or orange.
Natural Language Processing – NLP:
NLP is generally defined as software-based automatic recognition of speech and text. An already noticeable effect of this technology are the ever improving spam filters of our e-mails.
With a camera and digital signal processing, visual information is captured and analyzed. It can be compared to human vision, but it is not bound to human limitation. Usually the best possible results are achieved through machine learning, so we can say that these two areas are linked.
An engineering division focused on the design and manufacture of robots. Robots are often used to perform repetitive or difficult tasks for humans.
They are used, for example, to move large objects in the field of automobile production. Researchers also use machine learning to build robots that can interact in social environments.
In the field of self-propelled cars, a combination of computer vision, image recognition and in-depth learning is used to ensure automated driving skills. This avoids collision with unforeseen obstacles while the vehicles are in a specific lane.
In which application areas is AI already used and which applications are there?
Today there are many applications for artificial intelligence in consumer and business environments, from Apple’s Siri to Google’s DeepMind. For example, Siri uses Natural Language Processing (NLP) to interpret and respond to voice commands. On the other hand, Google’s DeepMind uses Deep Learning. It is able to connect and achieve meanings without relying on predefined behavioral algorithms, rather than learning from experience and using raw data as input. Google has improved the efficiency of its own power centers by applying DeepMind results and reduced energy consumption for cooling systems by 40%. Artificial intelligence in the business world enables companies to work smarter and faster and achieve more with far less. More and more companies are looking for powerful, advanced solutions that improve and streamline operations as technology and society evolve.
Finance & Banking Sector – Detection of fraudulent activities
Many banks use the various applications of artificial intelligence to detect fraudulent activities. A very large sample of data is passed on to the AI software, which includes fraudulent and non-fraudulent purchases and is trained to use data to determine whether a transaction is valid. The software becomes very skillful over time in detecting fraudulent transactions based solely on what it has learned before.
Financial services companies use AI-based natural language processing tools to analyze the brand sentiment of social media platforms and provide actionable advice.
Investment companies such as Aidya and Nomura Securities use AI algorithms to trade autonomously, and Robo traders to perform high-frequency trades with higher profits.
Fintech companies such as Kensho and ForwardLane use AI-driven B2C robot consultants to improve decision rebalancing and portfolio management by human analysts. Wealthfront uses AI algorithms to track account activity and help financial advisors adjust their advice.
Chatbots based on natural language processing can serve bank customers quickly and efficiently by answering common questions and providing timely information.
Many websites now offer a form of chat feature that allows customers to talk to a customer support representative or sales representative. In most cases, these conversations are initiated by some form of automated AI. Since these AI chat bots are able to understand the natural language, i.e. the human conversation, they can help customers find out what they need to know, extract information from the website and direct it to the appropriate web page or person for further assistance.
As cyber attacks become more common and more sophisticated tools are used to break cyber defenses, human actors are often no longer sufficient. Top companies around the world are investing heavily in cyber security to protect their data. Real-time threat detection, mitigation and, ideally, prevention are what businesses increasingly need. Using machine learning algorithms and applying large amounts of data to these algorithms, IT and security professionals can teach the AI solution to monitor behavior, detect anomalies, adapt, and respond to threats and alerts.
Automatic Exploit Generation (AEG) is a bot that identifies vulnerabilities in software bugs. When a vulnerability is detected, it is automatically backed up by the bot. AEG systems help develop automated signature generation algorithms that can predict the likelihood of cyber attacks.
The Computer Science and Artificial Intelligence Laboratory (CSAIL) of PatternEx and MIT has developed an AI platform called AI2 that predicts cyber attacks better than existing systems.
Active Contextual Modeling, has developed a continuous feedback loop between a human analyst and the AI system, which has a 10-fold higher attack detection rate than pure machine learning solutions.
The AI has quickly become a key component in the cyber security infrastructure of many enterprises, enabling a robust and sophisticated multi-layered security strategy.
In the healthcare industry, AI and ML technology has been particularly useful because it generates huge amounts of data to train with and allows algorithms to recognize patterns faster than human analysts.
Medecision developed an algorithm that recognizes 8 variables in patients with diabetes to determine whether a hospital stay is necessary.
The BillScreen app uses a smartphone camera, ML tools, and computer vision algorithms to detect elevated bilirubin levels in the sclera (white part) of a person with whom people are examined for pancreatic cancer. There are no telltale symptoms of this cancer and therefore he has one of the worst cancer prognoses.
NuMedii, a biopharmaceutical company, has developed an Artificial Intelligence for Drug Discovery (AIDD) platform that uses rich data and AI to understand the relationship between disease and drugs at the system level.
GNS Healthcare uses ML algorithms to find the most effective treatment methods for patients.
Landing.ai claims to have developed machine vision tools to detect microscopic defects in objects such as printed circuit boards using an ML algorithm trained with small sample image volumes. In the future, it will be possible to develop self-propelled robots that can move finished products without endangering anyone or anything.
Robots are often stationary in factories, but still run the risk of colliding with objects around them. A new concept enabled by the AI, called collaborative robots or “cobots”, can pick up instructions from humans, including instructions that have not been exposed to the robot before, and work productively with them.
AI algorithms can influence the manufacturing supply chain by identifying demand patterns across geographic, socio-economic segments and time periods and forecasting market demand. This in turn has an impact on warehousing, the procurement of raw materials, financing decisions, staffing, energy consumption and plant maintenance.
AI tools help predict plant malfunctions and failures and take or recommend preventive action as well as monitor operating conditions and plant performance.
Machine learning algorithms can improve the management of your inventory by retailers and other businesses. It can automate replenishment requests and optimize supply chains. You can transfer your inventory management and supply chain decisions to AI-based applications. Large AI vendors like IBM are investing heavily in supply chain management. AI in Supply Chain Optimization supports companies in the management and fulfillment of their order automation.
Advantages of AI Technology
Artificial intelligence is complex in nature. It uses a very complicated mixture of computer science, mathematics and other complex sciences. The technology helps to replicate the cognitive abilities of humans.
Improvement of the individual shopping experience
The provision of tailored marketing increases engagement, contributes to customer loyalty and improves sales. One of the advantages of using AI is that it can identify patterns in browser behavior and customer buying behavior. AI is able to create highly accurate quotes for individual customers using millions of transactions stored and analyzed in the cloud.
Automation of customer interactions
Most customer interactions currently require human participation, such as email, online chat, social media conversations and phone calls. However, the AI enables companies to automate this communication. It is possible to program computers to respond accurately to customers and process their requests by analyzing data from previous communications. The more the AI platforms interact, the better they become (especially in the field of machine learning). One example is AI chatbots that, unlike humans, can simultaneously interact with unlimited customers and both react and initiate communication, whether on a website or an app. It is estimated that intelligent machines capable of replicating human functions will take over 85 percent of all customer interactions by 2020.
The AI is also useful for companies that have to communicate with a large number of customers on a daily basis. For example, companies in the transportation industry, bus, rail and airline companies that transport millions of passengers daily can interact with AI in real time to send personalized travel information such as delay notifications. For example, some bus companies are already tracking their bus location and using AI to provide travellers with real-time information on where the bus is along its route and what time of arrival it is expected to have. It is important to stress that some of the possibilities have been available for years. However, systems that use AI technologies are more efficient and precise.
One of the biggest advantages of using cloud-based AI is that artificial intelligence applications can quickly discover important and relevant results in data processing. This can provide companies with previously undiscovered insights that can help them gain a market advantage.
The AI is able to control other technologies that increase the automation of business operations. For example, the AI can use intelligent heaters to maintain ideal temperatures. Similarly, robots already in use can be controlled (provided they are networked). In Japan, human-looking robots now serve as receptionists in some hotels in countries that automate check-in, book services and handle customer inquiries (in four languages). In retail, the AI is also linked to RFID and cloud technology for inventory tracking.
Another advantage of the AI is the possibility to predict data analysis based results. For example, it recognizes samples in the customer data that can show whether and in what quantities the current products are expected to be sold. It can also predict when demand will decline. This can be very useful to help a company to buy the right quantities. It is predicted that the days of seasonal sales will be over within 10 years because the optimization of residual stocks will reduce them to a minimum. This predictive capability is not only useful in retail. AI is also used in many other areas, such as banking, where currency and stock price fluctuations can be predicted, or healthcare, where it can accurately predict outbreaks of infection by analyzing social media content.
AI technologies can help companies automate the process of recruiting new employees. It can go through applications quickly and automatically rejects applications that do not meet the company's individual requirements. This not only saves time (or money for recruitment) but also ensures that the selection process is not discriminatory or distortive. The available AI programs can even take over the many administrative tasks of recruitment.
Implementation of AI technologies
Observing, learning and experimenting with current AI technologies is a good way to define an implementation strategy. This will help to avoid expensive and ineffective AI technologies. Try to find out how AI can benefit your business and how it can be integrated into core processes. To modernize your IT environment, start with the results you want to achieve. Remember that AI will not necessarily replace human operators in the near future, but it will enable companies and their employees to achieve significantly more in less time.
In the following, we have described possible steps that can help in the implementation of AI technology.
Bringing together experts
The first step is to find experts (internal and/or external) who can help companies identify and implement the potential of AI technologies for their own business.
Select the processes you want an AI to execute
A company must evaluate which processes can be automated or supported. Once this has been done, it makes sense to evaluate how people have made decisions in the past that will be supported or adopted by an AI in the future. The resulting process landscapes serve as templates for the AI implementation.
Determine how decision processes should be tracked or stored.
Questions of this kind will help you to identify decision-making processes. In addition, it makes sense to outline what non-human data the AI can use to make decisions.
Companies that rapidly implement artificial intelligence applications are likely to gain competitive advantage in the future. However, as the AI evolves rapidly, the challenge is to ensure that the organization has the necessary strategies and plans to support the AI capabilities as they become available. It is also important to ensure that the right technical infrastructure is in place to support the implementation of CI. For many companies, it is not a question of whether they want to introduce AI, but when. On this basis, monitoring the development of AI technology and planning is required well in advance.