Know about the scope, importance, features, types, and best examples of AI Software! The first industrial revolution was marked by the evolution of steam and water power, the second one was followed by electricity, the third one was marked by computing giving way to the fourth industrial revolution which will feature and enhance Artificial Intelligence and Big Data. We are now living in times where technology allows us to communicate and tell stories which otherwise would have never been possible to document. The inclusion of artificial intelligence in daily lives has helped humans to have a digital assistant who thinks in the same way and helps them with problem-solving, learning, planning, decision making via speech recognition sensors. AI Software is computer programs that possess and mimic near-human behaviour with the help of learning various data patterns and similar insights.
The auto industry is currently experiencing a rapid shift to autonomous vehicles (AV). This evolution is spearheaded by new, innovative technology companies that are bringing cutting-edge automotive platforms to the market at an unprecedented pace. Currently, vehicles on the road are equipped with the ability to maneuver on their own on highways while in the presence of a human driver. The next logical step in the race to autonomy is self-driving capability in an urban setting -- first with a driver and eventually with humans acting solely as passengers. However, driving in cities is an exponentially more difficult problem to solve than maneuvering on highways.
While most of the Machine learning articles are focussed on self-driving cars, GAN, and Image recognition, there are some other important areas that AI researchers and data scientists are working on. This includes researches to solve anomaly detection, which helps in network security to preventing financial fraud protecting businesses, individuals, and online communities. To help improve anomaly detection, Siddharth Bhatia (Ph.D. candidate) and his team at the National University of Singapore, have developed MIDAS (Microcluster-Based Detector of Anomalies) in Edge Streams. MIDAS is a new approach to anomaly detection that outperforms baseline approaches both in speed and accuracy. What makes MIDAS different from other available tools is its ability to detect these anomalies in real-time at speed greater than existing state-of-the-art models.
"Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library."
Industry 4.0 signifies a seismic shift in the way the modern factories and industrial systems operate. They consist of large-scale integration across an entire ecosystem where data inside and outside the organization converges to create new products, predict market demands and reinvent the value chain. In Industry 4.0, we see the convergence of information technology (IT) and operational technology (OT) at scale. The convergence of IT/OT is pushing the boundaries of conventional corporate security strategies where the focus has always been placed on protecting networks, systems, applications and processed data involving people and information. In the context of manufacturing industries with smart factories and industrial systems, robotics, sensor technology, 3D printing, augmented reality, artificial intelligence, machine learning and big data platforms work in tandem to deliver breakthrough efficiencies.
It was soon evident that embracing digital technologies and using AI-driven analytics was the only way to remain buoyant and navigate the disruptions. Several companies worldwide have already transitioned to the work-from-home concept and have adapted to the modern distributed work ecosystem (See: How is digital transformation shaping the new future?). For CIOs, realigning priorities and accelerating enterprise innovations continue to be a roller-coaster experience amidst these unprecedented times. More and more enterprises are now leaning on data science and analytics to optimize business performance and drive growth. With virtual communication taking the center stage, there is a growing emphasis on implementing AI-based workforce analytics and business intelligence solutions to fast-track digital transformation and generate deeper operational insights to respond faster and steer the volatile economic landscape.
When COVID hit the world a few months ago, an extended period of gloom seemed all but inevitable. Yet many companies in the data ecosystem have not just survived but in fact thrived. Perhaps most emblematic of this is the blockbuster IPO of data warehouse provider Snowflake that took place a couple of weeks ago and catapulted Snowflake to a $69 billion market cap at the time of writing – the biggest software IPO ever (see the S-1 teardown). And Palantir, an often controversial data analytics platform focused on the financial and government sector, became a public company via direct listing, reaching a market cap of $22 billion at the time of writing (see the S-1 teardown). Meanwhile, other recently IPO'ed data companies are performing very well in public markets. Datadog, for example, went public almost exactly a year ago (an interesting IPO in many ways, see my blog post here).
Data Science has proven to be a boon to both the IT and the business. The innovation incorporates acquiring value from information, understanding the data and its patterns, and afterward anticipating or producing results from it. Data scientists play a fundamental job in this since they are responsible for organizing, evaluating, and studying data and its patterns. Not just having suitable qualifications and education, a successful data scientist must be skilled at a specific set of tools. He should be conversant in at least one of the tools from the lifecycle of a data science journey, in particular: data acquisition or capture, data cleaning, data warehousing, data exploration or analyzing, and finally, data visualization.
Why does a secondary data store matter for AI? In my previous blog in this data store series, I discussed how the real selection criteria for an AI/ML data platform is how to obtain the best balance between capacity (cost per GB stored) and performance (cost per GB of throughput). Indeed, to support enterprise AI programs, the data architecture must support both high performance (needed for Ai training and validation) and high capacity (needed to store the huge amount of data that AI training requires). Even if these two capabilities can be hosted on the same systems (integrated data platform) or in large infrastructures, they are hosted in two separated specialized systems (two-tier architecture). This post continues the series of blogs dedicated to data stores for AI and advanced analytics.
Hiroshige Seko, the minister of Economy Trade and Industry (METI) of Japan introduced a new concept for their roadmap to realize'Society 5.0' the future urbanism as the next big thing in industries. He mentioned that we require another industrial revolution using advanced technological innovations including, AI, IoT, and Big Data; this would be'Connected Industries.' This was the inception of'Connected Industries' as introduced by Hiroshige with the impact on future lives. Artificial Intelligence or AI will be on a next-level role in this development, with a more significant impact on each ecosystem entity. Before moving ahead to understand the role of AI in the'Connected Industries', let's first understand AI and its applications.