As we have arrived at the era of AI Machine Learning and Smart Devices, It is quite obvious that every sector will be implementing it into their systems. Finance is the sector where the most amount of resources is put at first. So quite naturally artificial intelligence for finance and how it will affect the market is a huge topic nowadays. The term AI was first introduced by John McCarthy in 1956. As its name suggests it works by replicating human thinking capabilities.

Kim Custeau, senior vice president of asset performance management and manufacturing execution systems at AVEVA, explains why performance intelligence will be critical in a post-pandemic industrial world that demands organisations develop a high level of agility, sustainability and resilience. Why is performance intelligence a critical focus today? The global big data and analytics market is growing at lightening pace and projected to be worth $274 billion by 2022. Staying ahead of the curve requires a new understanding of the scope and scale of industrial information to leverage that data effectively. Insight into industrial information from edge to enterprise reduces downtime, production costs and energy consumption, allowing organisations to optimise resources and drive sustainability.

If you Google "what is art", this is the first definition you see: But AI generated "artwork" takes a new spin on this definition. In most artistic computer programs, human artists and programmers are still heavily involved throughout the production process. First, the human team must build various algorithms that train the bot to visualize a certain aesthetic. These algorithms are based on careful analyses from thousands of other art samples. The human artists are also needed in order to select which art samples to use in supposed algorithm.

Artificial intelligence is spearheading the Fourth Industrial Revolution, the latest era of technological advancement. The growing appeal and utility of machine learning-based systems is undeniable. As Christina Pazzanese wrote in the Harvard Gazette in October 2020, "Worldwide business spending on AI is expected to hit $50 billion this year and $110 billion annually by 2024 ... according to a forecast released in August by technology research firm IDC .... The company expects the media industry and federal and central governments will invest most heavily between 2018 and 2023 and predicts that AI will be'the disrupting influence changing entire industries over the next decade.'" However, as AI becomes more prevalent, we need to consider its potential effects on society and organizations, as well as people.

The ability to build artificial intelligence (AI) or machine-learning (ML) models is moving quickly away from the data scientist's domain and toward the citizen developer. Creating results from AI is getting easier, thanks to open-source tools that can convert AI/ML data streams into clear information that drives visualizations. It's essential to visualize AI and ML data in a way that helps you draw insights and find trends and patterns. The quality and quantity of the data available to you are critical factors. A visual representation should have some basic features.

The'mobile first' movement has resulted in most UX investments being focused on smartphones, tablets, smart home devices, etc. However, the faithful computer and laptop continues to be the workhorse of the masses and is where the most demanding and high-security tasks are performed. So why is it that there are no user-friendly and secure solutions for authenticating into computers and laptops? The insecurity of passwords is a UX problem, and shortcuts to make them easier lead to security risks, which lead to breaches. Most of the much-publicized mega-data breaches the past few years have been because of compromised or stolen passwords.

The 2019 edition of Opus Research's Decision Makers' Guide to Enterprise Intelligent Assistants report determined [24]7 AIVA to be a top solution for enterprises, and the only virtual agent solution capable of delivering across a breadth of simple FAQs to complex, conversational issues to online transactions. The Opus report presents a comprehensive assessment of 16 enterprise-grade Intelligent Assistant solution providers, with a focus on natural language processing, machine learning, AI, analytics and customer management integration to power digital self-service solutions. The report highlights [24]7 AIVA's ability to support both voice and digital channels and deliver unified self-service, calling out the company's differentiators as being a unique blend of AI and human insights, two decades of unparalleled experience in customer journeys across all channels, and proprietary insights including more than 150 patents and patent applications. "We analyzed a short-list of the leading providers in natural language processing, machine learning, AI and analytics to develop the industry's most comprehensive assessment of today's virtual agents and digital self-service solutions," said Dan Miller, lead analyst, Opus Research. An agent can take over a bot conversation at any time, and hand the conversation back to the bot to complete the interactions.

The world is awash in data like never before. From a person's morning Uber ride and favorite coffee spot, to the emails sent from their office--all these activities create massive amounts of data, but also behavioral and investment insights. Warren Buffett's investment style exemplifies the fundamental approach: "Which companies offer the best returns?" On the other hand, hedge fund manager James Simons of Renaissance Technologies is a notable example of the quantitative approach: "What is the best way to predict returns?" Both techniques have one thing in common--they seek excess return from the marketplace, or what is known as "Alpha". Today's infographic from GoldSpot Discoveries outlines quantamental investing as the blending of these two styles, human insight with computer power.

AI is real to many of us in business. Yet much of the debate about machine learning, AI and the use of Big Data remains hyperbolic. Headlines screeching about robot takeovers and mass job losses might do well for click-rates. Data specialists and early adopters may wax lyrical about the technological advances being made, and how these processes far outstrip the capacity, speed and computational power of mere mortals. But both polarities are far behind the more complex, interesting reality – and they're missing out on the real news for us humans.

This is the first of a series of articles intended to make Machine Learning more approachable to those who do not have a technical training. I hope it is helpful. Advancements in computer technology over the past decades have meant that the collection of electronic data has become more commonplace in most fields of human endeavor. Many organizations now find themselves holding large amounts of data spanning many prior years. This data can relate to people, financial transactions, biological information, and much, much more.