Applied artificial intelligence is transforming our world with practical use cases. 

Artificial intelligence is making machines around us smarter. While this is common knowledge, what many don’t understand is that artificial intelligence is more of a concept, and applied artificial intelligence is what puts AI to work in reality. Applied AI leverages the capabilities of software applications and powers machine learning, making it highly accurate and adaptable. It is applied AI that is transforming business processes as well the modern society. 

Several benefits like accuracy, cost-saving, and better decision-making come bundled with Applied artificial intelligence. In a dynamic business landscape, these are the benefits applied AI offers to industries. 

1. Better Decision Making: A lot of tasks would happen smoothly if machines had human-like judgment capabilities and that is exactly what applied AI brings to the forefront. It ensures reduced errors, predicts close to accurate outcomes, achieves end-to-end process automation, and creates a smart ecosystem. 

2. Perfecting Machinery: Applied AI bridges the gap between the digital world and the machine world while reducing errors, social ethics, and human bias in the process. 

3. Sharp Efficiency: Throughout all the stages of business processes, Applied AI accelerates efficiency while saving time, effort, and money. 

4. Optimized Automation: Applied AI helps in automating mundane and repetitive tasks to free up employees. While computer systems powered by AI handle tedious tasks, employees can devote their time and effort elsewhere to increase the business ROI. 

5. Increased Profits: Applied AI boosts profitability by identifying and solving complex business problems, faster than humans, through its machine learning capabilities. 

Applications of Applied AI 

Applied AI is put to work in various forms, depending on its purpose. These forms include natural language generation, chatbots, speech or image recognition, and sentiment analysis. This technology has become so omnipresent that it has made its place even in the creation of CRM platforms that allow better customer handling and lead to increased customer satisfaction. Industries like marketing use applied AI to target the right advertisement to the right audience, the education industry uses applied AI to decide the right curriculum, law enforcement uses chatbots for threat detection, finance uses applied AI for analyzing trade trends, the manufacturing industry uses applied AI for logistical support, and the healthcare industry uses applied AI for early detection and disease diagnosis, amongst many other uses. 

At the rate at which artificial intelligence is advancing, applied AI will see roaring use cases in the future, which is unthinkable at the moment. It is truly one of the most phenomenal and disruptive technologies of our time with immense capabilities to transform the world as we know it.

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Patterns and anomalies in big data can help businesses target likely customers, reveal fraud or even predict drug interactions. Unfortunately, these patterns are often not easily observable. To extract the needles of useful information out of haystacks of data, data scientists need increasingly powerful methods of machine learning.

Dr. Aria Nosratinia, the Erik Jonsson Distinguished Professor of electrical and computer engineering at The University of Texas at Dallas, has received two grants from the National Science Foundation totaling $749,492 to uncover relationships hiding in big data via machine learning and to develop methods to keep data communications safe.

“The contribution of my lab is to expand the universe of tools and techniques so we can discover new connections in the data,” said Nosratinia, who is associate department head of electrical and computer engineering in the Erik Jonsson School of Engineering and Computer Science.

Many machine learning and data mining algorithms use graphs, which are simply lists of connections between people, groups or objects. Examples include “friend,” “like” or “follow” relationships in social networks, or the list of videos streamed or marked as favorites in a streaming subscription service.

These mountains of data hide useful information whose extraction belongs to an area known as graph inference. Graph inference has many interesting and useful applications—for example, suggesting movies in a streaming service based on viewing history or purchasing suggestions in online shopping. It also can reveal patterns in the spread of epidemics, or provide insights into the folding of proteins, which is important in understanding how proteins function.

Nosratinia’s work for the first time proposes and analyzes techniques to improve graph inference by absorbing nongraph information, whose efficient blending with graph information was previously not well understood. Examples of non-graph information include a person’s age and residence ZIP code, which are individual attributes.

“In almost every practical application involving graphs, there exist nongraph data of great relevance,” Nosratinia said. “The kind of work we do is further upstream, developing the mathematical models, theory and techniques, but it has widespread applications.”

In several published works, Nosratinia describes the mathematical models he and members of his lab have developed that can improve the estimation of the information hidden in the graph with the aid of side information. Nosratinia and co-author Hussein Saad Ph.D.”19, now a senior engineer with Qualcomm Inc., recently analyzed how to identify a small cluster or community hidden in a large graph. Their latest work appeared in the December 2020 issue of the journal IEEE Transactions on Information Theory.

The second component of Nosratinia’s research addresses data security. His work harnesses the natural variations of wireless channels to provide layers of security for data transmission. This area of work, known as physical layer security, aims to leverage the imperfections of the communication channel as a tool for security. Part of this research is aimed at developing techniques for making the presence of electronic communication undetectable to cybercriminals.

“To give a simple example, a password works by leveraging the difference between what is known by a legitimate user versus cybercriminals who want to steal information,” Nosratinia said. “Our work creates, amplifies and analyzes statistical asymmetry of information against adversaries in ways that do not involve passwords or keys, and uses them for securing communications.”

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The discovery by a bunch of researchers reveal how AI can now read and interpret our personal choices

Artificial Intelligence has been disrupting many industries, business processes, and our lifestyle. With artificial intelligence technology, it is now possible to augment human intelligence and use it in decision-making and customer interactions. The ongoing digital transformation has brought many cutting-edge technologies to the mainstream and stressed the significance of AI and Big Data in revolutionizing industries. The role of artificial intelligence in business has been proved to be positively redefining operations and encouraging cost-efficiency.

But there are still areas connected to AI that researchers are studying to enhance the simulation of human intelligence to an extent, which enables sentiment analysis. Although researchers at the University of Helsinki and the University of Copenhagen have come up with an interesting discovery, wherein AI can read the brainwaves to understand and define subjective notions. In a paper published by these universities, AI can interpret the data generated from a brain-computer interface to build facial images that appeal to or attract different individuals.

A brain-computer interface (BCI), also known as brain-machine interface technology, is a communication system that connects the brain with an external machine or device. A brain-Computer interface is capable of measuring the activity in the Central Nervous System (CNS). This measured brain activity is converted into electronic and software signals that can be interpreted by AI.

Electroencephalography (EEG) and electromyography (EMG) are already in use by doctors to understand the neural activities of our brain and muscles, respectively.

BCI is extensively used in the healthcare and medical fields to treat broken neural connections between our brain and other body parts.

How interesting is it that this technique literally explains the old proverb, ‘beauty is in the brain’? Beauty is in fact inside our brains, which can now be interpreted by some machines and the wide range of AI applications can enable this.

But jokes apart, this study opens up new avenues for artificial intelligence, machine learning, and data analytics and also. According to a Daily Mail report, “The team strapped 30 volunteers to an electroencephalography (EEG) monitor that tracks brain waves, then showed them images of ‘fake’ faces generated from 200,000 real images of celebrities stitched together in different ways.”

The machine learning model called Generative Adversarial Neural Networks was trained to familiarise with individual preferences of faces so that it could easily generate new facial dimensions according to the brainwaves.

A report by Technology Networks revealed that the researchers developed new portraits for each participant, to test the validity of their modeling, and predicted that they will personally find these models attractive. Further, the researchers tested them in a double-blind procedure against matched controls to find that the new images match the preferences of the subjects with an accuracy of over 80%.

Connecting artificial neural networks to our brain can now produce results based on our personal preferences through a non-verbal communication process. This development is new since the neural networks or BCIs couldn’t peek into our personal choices and only establish the pattern of activities.

If it is possible to understand something this unique and personal, AI is not very far from augmenting and understanding the human brain to a more satisfying extent. However, such an invasion of artificial intelligence and technology into the internal structures of our brain will raise concerns about privacy and ethics. This new development will enable the understanding of individual and subjective biases that are internalized deep in our brains. Well, these innovations and developments in the field of AI will aid AI companies in expanding their business avenues and services.

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Deep learning technology and neural networks are powerful subsets of artificial intelligence that are majorly effective in real-world applications. But, its value is often overlooked. So, what is deep learning and why should it matter to you?

So what is deep learning? Jason Brownlee PhD, a professional developer and machine learning practitioner, defines deep learning as a “subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks”. 

In this sense, the artificial neural networks mirror how neurons in the brain would respond as they take in data, extracting the most relevant information and sending this information to the next node in the hierarchy. 

As the network progresses through the hierarchy, it becomes more complex. At the first level, the network learns something basic. In the next level, it takes this simple knowledge and builds on it, and so on. This process continues until the network reaches the top of the hierarchy. 

In its real-world application, deep learning allows for major technological advancements, like self-driving cars, or more practical iterations, like virtual assistants such as Siri or Alexa. 

In a business, deep learning can be used to train natural language processing systems to “understand text beyond simple definitions, read for context, sarcasm, and understand the actual mood and feeling of the writer”, says the team at MonkeyLearn.

This is particularly useful when conducting sentiment analysis. To find out more about the benefits of deep learning and its positive influence on business, media update’s Taylor Goodman spoke to Mary-Anne Piasecki, senior media analyst at Focal Points, a media intelligence and brand agency.

In this insightful Q&A, Piasecki discusses the differences between deep learning and machine learning, as well as the role of deep learning technologies when conducting media analysis.

1. How is deep learning different from machine learning?

Deep learning is a subset of machine learning whereby a human is needed to teach the AI (artificial intelligence), which only works on what it is taught. The AI, through algorithms, is able to learn from data and provide results based on its learned experience.

Deep learning is also able to improve its learnt experiences without humans needing to amend the algorithms. This technology works best with large amounts of data and can ‘solve problems’ even when working through different/diverse data sets.

2. How has the concept of deep learning transformed the way businesses conduct media monitoring?

Deep learning is beneficial to media monitoring as it is able to work through complex and diverse data within the media. At Focal Points, we work with a multitude of topics and information that require a human to go through manually and understand (in depth) before being able to provide concise analysis. 

Human learning, and machine learning, is a timely process, and through deep learning clients are able to receive information at an advanced speed that would not be possible manually.

3. What role does deep learning and neural networks play in sentiment analysis? 

We live in a world where information needs to be available at our fingertips, and as discussed before, up to thousands of media pieces for a human is a lengthy exercise.

Clients dealing with campaigns and crisis management need answers available immediately in order to react and implement their communication strategies. Through deep learning, we can provide those real-time results as the AI algorithms are learning and continuously developing themselves. 

4. What is beneficial about using deep learning technologies in media analysis? 

It speeds up manual work. Through using technology as our friend, we are able to improve the speed at which we understand data. 

This allows us to spend more time on providing insights that matter and less time sorting through raw data. Sentiment is of growing importance to our clients, and so is having information readily available.

Thus, with deep learning, we can spend more time on the insight into their sentiment (the why and what of the data) than the manual process of allocating it.

How do you use deep learning technologies and AI in your daily life? Be sure to let us know in the comments section below.

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Artificial intelligence (AI) and machine learning (ML) are positioned to disrupt the way we live and work, even the way we interact and think. Machine learning is a core sub-area of AI. It makes computers get into a self-learning mode without explicit programming.

At this point, most organizations are still approaching ML as a technology in the realm of research and exploration. In this first article of a series, we delve deeper into the world of machine learning and its applications. The following articles will focus on building an ML implementation plan. In doing so we not only understand the concepts behind the technology, but also why it can make the difference between keeping up with competition or falling further behind.

What Is Machine Learning?

Gartner defines machine learning as: “Advanced learning algorithms composed of many technologies (such as deep learning, neural networks and natural language processing), used in unsupervised and supervised learning, that operate guided by lessons from existing information.”

Machine learning is the process of teaching computers to develop intuitive knowledge and understanding through the use of repetitive algorithms and patterns. Machine learning in lay-man’s terms is the process of schooling a repetitive activity to a dumb system that needs to develop some innate intelligence. The goal is to feed the system large amounts of data so it learns from each pattern and its variations, so it can eventually be able to identify the pattern and its variants on its own. The advantage a machine has over the human mind here is its ability to ingest and process large amounts of data. The human brain, although limitless in its capacity to ingest data, may not be able to process it at the same time and can only recall a limited set at one time.

There are three key types of machine learning: supervised, unsupervised and reinforced.

• Supervised Learning:Is the most prevalent form of machine learning today. In this kind of learning the data is labeled to tell the machine exactly what patterns it should look for. This is the kind of learning used by Netflix or Amazon when they look for similar shows to watch or similar products to shop for.
• Unsupervised Learning: Requires no labels for any of the input data. The machine just looks for whatever patterns it can find. The goal here is to introduce the algorithm to multiple groups/types of information and then establish labels based on what is “learned” by the algorithm. Unsupervised learning algorithms aren’t designed to single out specific types of data, they simply look for data that can be grouped by similarities, or for anomalies that stand out. It is akin to letting a child look at different objects and then classify them according to color, function, entertainment value, etc. Unsupervised algorithms are not as popular as supervised ones, however with the increasing use of ML in cybersecurity, operational improvement and automation etc. their applicability has increased. Unsupervised learning can in fact also be used to create and label data for supervised learning.
• Reinforced Learning:Is the latest frontier of machine learning and the least explored in terms of applicability as well as usage. Expectations are we’ll see a tremendous increase in reinforced learning as computing power increases and data volumes to feed into existing algorithms also increase. A reinforcement algorithm learns through measuring various aspects of data provided to it and then starts replicating these behaviors. It is similar to rewarding or punishing a child for its behavior. It is this kind of learning used for gaming such as Google’s AlphaGo, the program that famously beat the best human players in the complex game of Go.

Other aspects of machine learning include neural networks and deep learning.

Neural networks have been studies for a long time. These algorithms endeavor to recognize the underlying relationships in data, just the way the human brain operates.

Deep learning is a class of machine learning algorithms that involves multiple layers of neural networks where the output of one network becomes the input to another.

The key to understanding machine learning is to understand the power of data. These algorithms work by finding patterns in massive amounts of data. This data, encompasses a lot of things—numbers, words, images, videos, sound files etc. Any data or meta data that can be digitally stored, can be fed into a machine-learning algorithm.

Applications of Machine Learning

Machine learning, in conjunction with deep learning, have a wide variety of applications in our home and businesses today. It is currently used in everyday services such as recommendation systems like those on Netflix and Amazon; voice assistants like Siri and Alexa; car technology in parking assist and preventing accidents. Deep learning is already heavily used in autonomous vehicles and facial recognition systems. As the technology matures and receives widespread acceptance, we expect to see its applicability grow in these areas:

• Medical diagnosis and personalized medicine.
• Education and training, especially in the use of educational software for people with disabilities.
• Weather and storm prediction systems.
• Sensor technology.
• Building efficiencies into our agricultural, supply chain and maintenance systems.
• Fraud detection and market predictions.
• Speech and image recognition.

And many more ….

Machine Learning Is Here to Stay

The availability of widespread computing power though the use of cloud technologies along with an increasing volume of readily available data has driven a number of advancements in the field of AI and ML. Organizations need to first build an understanding of the technology itself, collaborate on building a vision for using the technology internally and then build an implementation plan collaboratively between business and IT. In part two of this ML series we will focus on building a vision and implementation plan.

About the Author

Geetika Tandon is a senior director at Booz Allen Hamilton, a management and technology consulting firm. She was born in Delhi, India, holds a Bachelors in architecture from Delhi University, a Masters in architecture from the University of Southern California and a Masters in computer science from the University of California Santa Barbara.

The views and opinions expressed in these articles are those of the author and do not necessarily reflect the official policy or position of her employer.

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