Explaining Traditional Engineering Models It is a well-known fact that models of physical phenomena that are generated through mathematical equations can be explained. This is one of the main reasons behind the expectation of engineers and scientists that any potential model of the physical phenomena should be explainable. Explainability of the traditional models of physical phenomena is achieved through the solutions of the mathematical equations that are used to build the models. Explanations of such models are achieved through analytical solutions (for reasonably simple mathematical equations) or numerical solutions (for complex mathematical equations) of the mathematical equations. Solutions of the mathematical equations provide the opportunities to get answers to almost any question that might be asked from the model of the physical phenomena. Solutions of the mathematical equations are used to explain why and how certain results are generated by the model. It allows examination and explanation of the influence and effect of all the involved parameters (variables) on one another and on the model's results (output parameters).

The Encyclopedia Britannica defines Artificial Intelligence or A.I. as "the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings." Intelligent beings are those who can adapt to changing circumstances. The most forward-thinking companies are investing in Artificial Intelligence, as they already realized the importance of A.I. in business, and the impact A.I. will have, while it is becoming a key component of organizations' strategies as digital disruption increases. I am sharing here today an overview of the top 5 companies in the world according to Fortune 2020) and some examples of how these companies are using A.I. to empower their business. Walmart has been in business since the 1960s, but the company is still developing ways to revolutionize retail operations and enhance customer service.

The supply chain is an ecosystem that affects businesses around the world, and the COVID-19 pandemic has thrown a monkey wrench into this previously undisturbed process. With region-specific restrictions, limited supply of certain goods, and a constantly changing consumer mindset, almost all businesses are playing catch up in addressing the needs of every consumer. Add to that the oil price war and the result is near chaos for both consumers and businesses. It may be a gamble to implement new supply chain systems in these circumstances, but it's a bet that could pay dividends not just now but in the long term. Artificial intelligence (AI) and data analytics tools can provide the much-needed push companies need to keep their businesses afloat--and maybe even thrive--despite the global crisis.

With the objective of establishing an ecosystem that will have "zero tolerance" for non-compliance with regulations, the corporate affairs ministry is betting big on AI and data analytics to deal with shell companies. Using these technologies, the ministry is developing an advanced MCA 21 portal. Used for submitting requisite filings under the companies law and managing a repository of data on corporates in India, the portal will enable authorities to weed out entities that do not comply with regulations. Typically, shell companies are floated for illegal activities like money laundering, and a zero tolerance approach to this, enabled by AI, can put a stop to these practices. It would make it "almost impossible for a shell company to survive," points out Corporate Affairs Secretary, Injeti Srinivas.

Continuing efforts to have a robust corporate governance system and ensure high level of compliance, the ministry is also in the process of having an advanced MCA 21 portal. The portal is used for submission of requisite filings under the companies law and is also a repository of data on corporates in the country. Corporate Affairs Secretary Injeti Srinivas told PTI that once the third version of MCA 21 becomes fully operational, the portal would make it "almost impossible for a shell company to survive." Generally, shell companies are those which are not complying with regulations and many such entities are allegedly used for money laundering and other illegal activities. Noting that the third version of the portal might be fully operational in a year from now, the secretary said the ecosystem would have zero tolerance for non-compliance.

SOMEBODY LESS driven than Tom Siebel would have long since thrown in the towel. In 2006 the entrepreneur, then 53 years old, sold his first firm, Siebel Systems, which made computer programs to track customer relations, to Oracle, a giant of business software. That left him a billionaire--but a restless one. In 2009, a few months after Mr Siebel had launched a new startup, he was trampled by an elephant while on safari in Tanzania. When, a dozen surgeries later, he could work again, the enterprise almost went bankrupt.

Figuring out the impact of new and emerging technologies on your business can be tough. In the next decade, emerging technologies will have a transformative impact in terms of business operations, software delivery and how you interact with your customers. For forward thinking businesses and early adopters, the commercial rewards associated with embracing the right mix of emerging technologies can be vast. That's why we've created the Waracle guide to the new digital enterprise. Once upon a time in the distant past, fax machines ruled the world and machine learning was something you did to figure out how to work the slide projector.

Recognizing the pervasivetalent gapthat exists between data scientists and data workers in the line of business, Assisted Modeling helps teach data science with a guided walk-through and aims to help all data workers, regardless of technical acumen, advance their skill sets in the process of building machine learning models. Our approach in building Assisted Modeling is to advance the skills of the data worker, creating next-level citizen data scientists capable of building the machine learning models required to tackle the advanced analytic challenges of the future. Assisted Modeling provides users the transparency and control needed to build trustworthy machine learning models that drive business outcomes without writing a line of code. As an output of the application, users can access code-free machine learning tools directly within the Alteryx Designer interface. Assisted Modeling allows any data worker to construct machine learning models, understand how and why their models work, and capture modeling decisions, turning raw data into informed business decisions with unprecedented speed and confidence.

SOMEBODY LESS driven than Tom Siebel would have long since thrown in the towel. In 2006 the entrepreneur, then 53 years old, sold his first firm, Siebel Systems, which made computer programs to track customer relations, to Oracle, a giant of business software. That left him a billionaire--but a restless one. In 2009, a few months after Mr Siebel had launched a new startup, he was trampled by an elephant while on safari in Tanzania. When, a dozen surgeries later, he could work again, the enterprise almost went bankrupt.

Data Analytics is no more the future. Many people believed that it is just a flash in the pan and will wear off in some time. That fact is settled, and big data and analytics are here to stay. It is estimated that the market will grow at a CAGR of 30 percent between 2017 and 2023. This speedy growth will propel the market size for Big Data to reach a size of $80 billion.