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Machine learning typically is used to solve a host of diverse problems within an organization, extracting predictive knowledge from both structured and unstructured data and using them to deliver value. The technology has already made its way into different aspects of a business ranging from finding data patterns to detect anomalies and making recommendations. Machine learning helps organizations gain a competitive edge by processing a voluminous amount of data and applying complex computations.

With machine learning, companies can develop better applications according to their business requirements. This technology is mainly designed to make everything programmatic. Applications of machine learning have the potential to drive business outcomes that can extensively affect a companys bottom line. The rapid evolution of new techniques in recent years has further expanded the machine learning application to nearly boundless possibilities.

Industries relying on massive volumes of data are significantly leveraging machine learning techniques to process their data and to build models, strategize, and plan.

While implementing the effective application of machine learning enables businesses to grow, gain competitive advantage and prepare for the future, there are some key practical issues in machine learning and their business implications organizations must consider.

As machine learning significantly relies on data, the occurrence of noisy data can considerably impact any information prediction. Generally, data from a dataset carries extraneous and meaningless information which can significantly affect data analysis, clustering and association analysis. Having a lack of quality data can also restrain the capabilities of building ML models. In order to cope with quality data and noise, businesses need to apply better and effective machine learning strategies through data cleansing and overall processing of data.

There is no doubt that the development of machine learning has made it possible to learn directly from data rather than human knowledge with a strong emphasis on accuracy. However, the lack of the ability to explain or present data in understandable terms to a human, often called interpretability, is one of the biggest issues in machine learning. The introduction of possible biases in data has also led to ethical and legal issues with ML models. Theinterpretabilitylevels in the field of machine learning and algorithms may significantly vary. Some methods are human-compatible as they are highly interpretable, while some are too complex to apprehend, thus require ad-hoc methods to gain an interpretation.

In the context of supervised machine learning, an imbalanced dataset often involves two or more classes. There is an imbalance among labels in the training data in several real-world datasets. This imbalance in a dataset has the potential to affect the choice of learning, the process of selecting algorithms, model evaluation and verification. The models can even suffer large biases, and the learning will not be effective if the right techniques are not employed properly. ML algorithms can generate insufficient classifiers when faced with imbalanced datasets. When trying to resolve certain business challenges with imbalanced data sets, the classifiers produced by standard ML algorithms might not deliver precise outcomes.

Thus, to address imbalanced datasets requires strategies like enhancing classification algorithms or balancing classes in the training data before providing the data as input to machine learning algorithms.

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Vivek Kumar is the President of Consumer Revenue at UpGrad, an online education platform providing industry oriented programs in collaboration with world-class institutes, some of which are MICA, IIIT Bangalore, BITS and various industry leaders which include MakeMyTrip, Ola, Flipkart to name a few. He has 19 years of experience in diversified industries like Consumer goods, Media, Technology Products and Ed-ucation Services. He has been leading businesses & multi-cultural teams with a consistent record of market-beating performance and building brand leadership. His previous engagement has been with Manipal Global Education services as Sr General Manager, Education Services (Digital Transformation Strategy & Global Expansion).

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Decoding Practical Problems and Business Implications of Machine Learning - Analytics Insight

The events of the past few months have taught us, among other things, how little control we have over our destiny. A major crisis, such as the Covid-19 pandemic, can come unexpectedly at any point of our lives and ruin everything we have worked to achieve. But as much as it may seem unfair, the unpredictability of future events and the constant change is the only thing that is certain.

Many things could have been done better. When it comes to the world of tech and AI many businesses will learn that it is worth investing in robust and bias-free machine learning solutions. On a larger scale, hopefully the world leaders will learn to take scientific data more seriously and with a greater sense of urgency. However, the reality is that no matter how much we invest in making our predictive modelling algorithms more accurate, change will always bring unexpected events our way.

The fact that change is the only thing we can be sure of is one of the key wisdoms of Vipassana meditation practice. Both good and bad things will keep on coming our way and we have to just observe and keep calm. Easier said than done? True, but meditation can help people find their path to equanimity. If you are not too familiar with meditation but have a good understanding of the world of AI and machine learning, there is an interesting connection between the two that can help you grasp the key principles of meditation practice.

Before going any further, it is necessary to clarify that meditation focuses on the brain and you cannot really compare a machine learning model to a human brain. To simplify, it would be a bit like comparing the first basic telephone from the 19th century with the latest iPhone 11 Pro. The original telephone was only capable of performing one task at a time whereas the iPhone is capable of multitasking and has complex functionalities which are not fully understood to most users. However, it is worth observing that the overarching process which describes many commonly used machine learning systems can also be used to describe the functionality of our brains.

A machine learning system consists of an input (i.e. data), algorithms that adapt and improve the more data you feed into them and an output which is a result of that process. Similarly with the brain there is an input in a form of sensory data (i.e. our senses, such as sight and sound) and neurons transmitting electrical signals in the brain that produce an outcome, such as your thoughts and actions.

Everything we experience throughout our lifetimes can be treated as input data and contributes to the shape and functionality of the brain. What is interesting in the case of a human brain is that some of the input data is processed consciously, however the majority happens sub-consciously without individual's awareness. This sub-conscious data processing in cognitive science is often referred to as priming and is a reason for another well-known concept in machine learning, namely bias.

People as well as algorithms are prone to making biased decisions. Some famous examples include: the familiarity bias[1] liking more what you already know, symmetry bias[2] perceiving symmetric faces as more attractive, other biases related to appearance such as perceiving wider male faces as less trustworthy[3], and many more incorrect or inaccurate inferences made based on first impressions. Everything we experience in life has an impact on our brain processing and therefore our decisions.

In machine learning, data scientists spend a lot of time and effort on data pre-processing and data mining to remove bias from the data. Similarly, Vipassana meditation practice focuses on peoples data input the five senses: sight, sound, smell, taste and touch. Throughout the meditation practice students are encouraged to sit still for hours at a time without any distraction and simply be aware of and observe the sensations of the body i.e., the data input. This is what is being fed into the brain at any given time, and should therefore require at least as much attention as the data fed into a machine learning system.

The overarching process is simple: you smell a flower -> feel a pleasant sensation in the brain -> you smile. The simplicity behind this input-output scenario (as well as many neuroscientific studies[4] which show that activity in the brain starts before people consciously realise what they are about to do) can help us understand and accept that the concept of conscious free will is an illusion[5].One of the key objectives of Vipassana practice is that the scientific laws that operate one's thoughts, feelings, judgements and sensations become clear. Life becomes characterised by increased awareness, non-delusion, self-control and peace[6].

The concept of free will and attaching too much importance to the idea of the self is a common source of unhappiness. Mr. Goenka, the Burmese-Indian teacher of Vipassana meditation points out that there is a tremendous amount of attachment towards this physical structure, this mental structure, by identifying oneself as I, I, I And the result is misery[7]. This is commonly seen in our society, people often attach their self-worth to imaginary physical or mental concepts such as their background, skin colour, religion, wealth or nationality. Too much focus on self-identity results in many social problems such as racism and identity politics.

Understanding the simplicity of the input-output scenario that describes our brain functionality can help us move beyond these made-up concepts that divide cultures and societies across the globe. Instead, we should perhaps take inspiration from a simple reinforcement learning system, reward the brain with positive experiences for ourselves and others and allow it to evolve in the direction of tolerance, understanding and compassion in order to find our path to equanimity.

[1] Newell, B. R., Lagnado, D. A., & Shanks, D. R. (2007). Straight choices: The psychology of decision making

[2] Little, A. C., Jones, B. C., Waitt, C., Tiddeman, B. P., Feinberg, D. R., Perrett, D. I., Apicella, C. L. & Marlow, F. W. (2008) Symmetry is related to sexual dimorphism in faces: data across culture and species

[3] Stirrat, M., & Perrett, D.I. (2010). Valid facial cues to cooperation and trust: Male facial width and trustworthiness.

[4] Haggard, P. (2008). Human volition: towards a neuroscience of will

[5] Wegner, D. M. (2002). The Illusion of Conscious Will. Bradford Books/MIT Press.

[6] Vipassana Meditation, As taught by S.N. Goenka in the tradition of Sayagyi U Ba Khin (https://www.dhamma.org/en/about/vipassana)

[7] Vipassana Meditation 10-day Course, S.N. Goenka

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Meditation And Machine Learning: A Guide To Acceptance And Equanimity - Forbes

Every now and then an opportunity comes along that you just cant pass up. One such opportunity that fell into my lap was when OReilly media reached out to me to see if I was interested in partnering on a collaborative book on the ethics that surround data science. For those who know me and follow my work, they have seen me calling for more focus on ethics for several years. Ive written blogs and papers on the topic, Ive given many conference presentations on the topic (including at Predictive Analytics World 2019!), and Ive had countless discussions with clients

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Carnegie Mellon University (CMU) and Facebook AI Research (FAIR) have developed a semantic navigation system SemExp, to train robots to recognise objects, using machine learning.

Through SemExp, a robot is trained to differentiate between a kitchen table and an end table, while it is also able to understand where these objects are likely to be found. The process allows the navigation system to think strategically about how to search for something, said Devendra S. Chaplot, a Ph.D. student in CMU's Machine Learning Department, in a release.

Classical robotic navigation systems, explore a space by building a map showing obstacles. The robot eventually gets to where it needs to go, but the route can be circuitous. The system uses its semantic insights to determine the best places to look for a specific object, Chaplot added.

By making the system modular, the overall efficiency has gone up. The robots can now focus on learning the relationships between objects and room layouts. It also enables the robot to navigate its way from point A to point B, in the quickest possible manner.

Going forward a navigation technology like this could improve the interactions between people and robots. While a robot could bring an item in a particular place or it could find its way when directed, said a CMU release.

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A navigation system powered by machine learning is training robots to recognise objects - The Hindu

By Louise UffelmanJuly 29, 2020

Working on a real-life project that will introduce students to how algorithms work in applications with crucial outcomes will provide them with the important skills that can transfer to other areas of computer and data science.

~ Moataz Khalifa

As the race for a COVID-19 vaccine continues, Moataz Khalifa, assistant professor and director of Data Education at Washington and Lee University, is involved in an equally promising research project that focuses on a non-invasive, early detection system of the virus.

In March, just as the numbers of cases were climbing around the world, Khalifa was invited by Wu Feng, Elizabeth & James Turner Fellow, professor of computer science at Virginia Tech and director of its SyNeRGy lab, to join his research lab to develop a deep-learning algorithm to enhance low-radiation CT scans of peoples lungs. Fengs current research was already investigating similar applications in CT scans of brain tumors, and he received two National Science Foundation grants totaling $250,000 to expand his project to work on the COVID-19 early detection system.

Currently, the genetic-based RT-PCR tests available to detect COVID-19 rely on swabbing the nasal cavity. With testing kits in short supply, accuracy of the results only around 59%, and increasingly long processing times of 10 to 14 days, the hunt was on for a system that was more accurate, available and faster to handle the growing demand for tracking infections.

Feng is excited to have Khalifa, who earned his Ph.D. in physics from Virginia Tech, join the team. My expertise is in parallel and distributed computing, and there are intersections with respect to data analytics and machine learning that weve got going on in our labs. Moataz has the skill set to look at different mathematical models used to build an algorithm to get inside the box so that we can build the best medical imaging software system possible.

CT scans, which combine X-rays and computers to capture 2-D cross-sectional images of the body, are able to detect the existence of COVID-19 through certain markers in the lungs. The initial studies from China and Europe on which Fengs team is building its research indicate that the detection using CT scans is possible in asymptomatic people and with higher accuracy (upwards of 90%) than any other test available, Khalifa said.

The barrier is getting people to the hospital for testing, but with a portable CT scanner, the testing can travel to where its needed, whether that be a college campus or an office complex. Portable scanners generally use less radiation and hence provide lower-resolution images. The algorithm Khalifa is working to modify will help enhance the quality of the images, improving the accuracy of detecting the coronavirus in its early stages. Another upside is that hundreds of people can be scanned a day, with results essentially available in real-time.

As the project moves forward, Khalifa will bring W&L undergraduates onto the research team. He noted that artificial intelligence and machine learning is a hot area right now. Working on a real-life project that will introduce students to how algorithms work in applications with crucial outcomes will provide them with the important skills that can transfer to other areas of computer and data science.

Khalifa added, W&Ls collaboration with Virginia Tech is a strong and mutually beneficial one, bringing us to the front lines of this fight against COVID-19. I cannot say enough about how exciting it is to be a part of it and to bring our university even closer to such a critical contribution during these times.

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Using Machine Learning to Track COVID-19 Washington and Lee University - Washington and Lee University News Office