With the requirement for AI growing by leaps and bounds, it is no wonder then that the market for AI technologies has been flourishing in recent times.
To understand what’s making the news in the AI world today, we take a look at some of the leading AI technologies that companies have adopted or considering to adopt in the coming time to further support and boost the human decision-making process.
Natural Language Generation (NLG)
Natural Language Generation, or NLG, is an AI technology that has been in use since the last couple of years. This AI technology is responsible for converting data into plain and simple English language. Basically, this means that NLG technology allows the software to look at any data and then derive a story from it, similar to what the work of a human analyst would be. Simply put, NLG is a means of storytelling from your data.
NLG acts like a translator and converts your computer data into text. By far one of the most successful and efficient NLG applications have been in the Data-to–Text systems, which churn out textual summaries from the datasets or databases provided.
Sometimes referred to as automatic speech recognition or even computer speech recognition, speech recognition is another AI technology that is today used widely. The basics of speech recognition revolve around the capability of a machine or computer software to identify the words and phrases being spoken and converting these words and phrases to a machine-readable format of text.
AI speech recognition has today become such an integral part of our day-to-day life that you will find speech recognition software installed in mobile devices and computers everywhere, allowing for easy access to this technology.
Hardware that is AI-optimized is today the hot topic across enterprises and the technology world. While traditionally it has been AI software that has always received the most attention from industry insiders as well as investors, however, the new generation of AI hardware technology is rapidly changing this mindset, taking its place under the spotlight as 2019 begins.
The new capabilities for AI-optimized hardware will be focused on providing a much faster insight into the preferences and behaviors of customers.
AI Virtual Agents
AI virtual agents also referred to as intelligent virtual agents (IVA), is present nearly everywhere today. An AI virtual agent is basically a chatterbot program, commonly known as a chatbot that functions as a virtual customer service agent. These virtual agents have changed the way in which companies function and made it easier for customers to reach out to a virtual company representative for the smallest of issue or question.
These programs are primarily being used in customer relationship management to help improve tasks such as answering product-related questions, making reservations, locating information, and even solving complaints.
AI Cyber Defense
As enterprises go digital and optimize their computer capabilities to leverage the business advantage that AI has to offer, the race is now on to achieve digital connectivity across the complete value chain. However, as companies move towards more and more digital connectivity, this has also provided a window of opportunity to cybercriminals. Cybercrime crews are today actively looking to target digital infrastructures such as cloud infrastructure, internet of things devices and software as a service offering, amongst others.
The latest advancements in AI have led to the development of a smarter and autonomous security system which are capable of learning for themselves (machine learning with AI). AI has also allowed security teams to easily detect any incidents of cyber breaches, issues, and incidents.
AI Machine Learning Platforms
AI has made it possible for computers to also start learning. What’s more, computers are, of course, incredibly intelligent and can process whatever is being taught at a rapid pace. AI-based machine learning (ML) allows us to develop techniques that make it possible for computers to learn.
A subset of AI, ML uses complex processes for learning equally complex decision systems, finding anomalies and patterns in datasets, and of course, raising an alert is needed.
AI and Decision Management
AI and decision management systems have today made it possible for companies to make valid and innovative decisions by providing them with the latest and most relevant information, along with performing analytic functions on their behalf.
By combining decision management information systems and AI, the entire process of decision-making has reached different levels altogether. The capabilities of AI helps these decision management information systems translate the customers’ data into predictive models that are based on key market trends. The primary benefits AI and decision management systems provide companies today are:
- Help make faster decisions
- Recognizing the potential market risks ahead of time
- Automating more processes
AI and Deep Learning Platforms
Until recently, deep learning was just a theory that researches around the world could only contemplate upon. Using deep learning platforms, computer systems are now capable of learning and recognizing patterns from data sets that were earlier considered to be too complex for even the expert-written software.
Deep learning platforms use a special type of ML that revolves around artificial neural circuits that are made up of several abstraction layers and can mimic the human brain. This allows for faster and easier data processing as well as the creation of patterns for faster decision making.
Peltarion, Saffron Technology, Sentient Technologies, Fluid AI, and many others are today offering several types of deep learning platforms that are definitely worth exploring.
Biometrics is the science that is based on the statistical analysis of biological characteristics such as fingerprint recognition and facial recognition. Combining AI and biometrics, it has now become easier for organizations to offer better security and convenience as compared to the traditional methods used for people recognition. AI-supported biometrics can be used to identify, analyze, and even measure human behavior and also study the physical aspects of the form and structure of an individual’s physical body.
AI-biometrics has allowed for a greater interaction to take place between computer systems and humans, including interactions that are related to image, speech, touch, and recognition of body language. The autonomy offered by AI systems makes it possible to perform without the need for human intervention, for example matching a face from one image to another image contained in a bigger database all by itself.
AI and Knowledge Workers
AI is now able to perform several mundane tasks in companies and is also being used for cognitive outsourcing. Keeping this in mind, it is no wonder then that the highly-valued employee of the future will not be humans, but instead, they will be the knowledge worker. However, while the debate rages on about whether or not AI will eventually replace humans in the workforce, many also advocate for the fact that AI has the potential to help employees in many types of processes, especially the ones who are in knowledge work.
The automation of knowledge work has already begun, with legal and medical professionals being heavily reliant on knowledge works. As time passes, more industries will start using AI as the main diagnostic tool as it makes data more accessible, more organized, and even massive datasets are no issue for AI.
AI and Content Creation
Perhaps one of the most difficult tasks facing Ai is the creation of content. However, advancements in NLP, image recognition, and ML has today given AI the ability to predict just what messages and what images will help drive towards the desired consumer actions.
One of the greatest examples of content creation using AI has been done by the Cola-Cola Company. Coca-Cola has successfully implemented automated narratives, including writing social media posts, creating scripts, and even choosing music.
Emotion Recognition and AI
AI has made it possible for computer systems to even read emotions on a human face with the use of advanced image processing along with audio data processing. AI has made it possible to capture micro-expressions, or the smallest of body language cues, coupled with vocal intonation that gives away a person’s emotions.
This technology is being used by law enforcers for detecting more data from a person during interrogation. Apart from security purposes, this technology has a lot of potential for marketers as well, allowing them to understand how excited, angry letdown, or how positive a person’s reaction is towards a new product.
Image Recognition and AI
Facial recognition or image recognition refers to the process of identifying an object or feature or a human in a digital image or video. Adding AI to the technology of image recognition has made it possible to search across social media platforms for photos and identifying who is who.
Not only on social media, but image recognition is also being used for detecting and capturing license plates, diagnosing illnesses, analyzing clients, verifying users through their facial features, finding uncategorized images, and many other processes.
AI and Peer-to-Peer Networks
Peer-to-peer networks are formed when you put together two or more computer systems together, connect them, and make it possible to share resources without having the data pass through another server computer.
Earlier version of peer-to-peer networks, like Napster, used client software and centralized servers to allow for huge amounts of data to be shared. Recent networks such as BitTorrent and Kazaa have done away with the centralized server and instead, split up the sharing duties between several nodes in order to free up bandwidth.
Robotic Process Automation and AI
Robotic Process Automation (RPA) makes use of scripts and other methods to mimic and automate tasks that have been traditionally delegated to humans in order to support various corporate processes.
RPA, together with AI, has the capacity to boost significant, step-change efficiencies and also generate totally new sources of value-add for enterprises. As the technology has progressed, the licenses for the robots that are used have also gone down significantly and is now available at just a fraction of the price of actually having to employ someone for that process.
Text Analytics and Natural Language Processing (NLP)
This AI-based technology makes use of text analytics to better understand the structure and formation of sentences, their meaning, as well as the intention behind the sentences. It makes use of statistical techniques and ML.
Text analytics and NLP is extremely helpful for companies that collect massive amounts of emails, data, documents, social media data, and other information that are text-based. Text analytics and NLP are today used by many security systems and fraud detection systems. This technology is also used by many types of automated assistants and apps that are used for extracting unstructured data.
Digital Twin and AI Modeling
Digital twin is a concept that is based on the internet of things (IoT), but it requires the skills of ML and AI together. While the concept of digital twins is not really new, it is today an integral part of digital enterprises.
General Electric (GE) has been one of the first movers into this industry, using AI and digital win concept to monitor its locomotives, aircraft engines, and even gas turbines. GE’s system can be used to predict failures through the use of cloud-hosted software models of the company’s machines.
AI and Compliance
Ever since we can remember, the process of compliance has been relying on people. The term compliance refers to the certification that confirms that an enterprise or a person meets certain requirements of the accepted legislation, practices, rules and regulations, or even the terms of a contract. There is usually a significant industry body behind this certification that upholds the criteria for compliance.
AI, through intelligent algorithms, has automated the process of compliance, reducing the burden on organizations the compliance professionals. For example, NLP solutions are being used for scanning regulatory text and matching the same pattern with keyword clusters that can identify any changes that are important for the company. AI has also reduced the occurrence of fraud in the entire process.
The Future belongs to AI
These above-discussed AI technologies have ensured that businesses benefit greatly from using AI and companies are thus ensuring that the obstacles in adopting AI are rapidly done away with to facilitate easy adoption of these technologies. Once the initial obstacles have been overcome, enterprises start gaining in many areas, thus allowing an overall AI driven transformation in their performance.
AI has indeed made the idea of an error-free world acceptable and very much possible. From small to big activities, companies and people are using AI for generating leads and removing work pressure.