If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
TensorFlow Extended (TFX), a TensorFlow based general-purpose machine learning platform provides orchestration of many components--a learner for generating models based on training data, modules for analyzing and validating both data as well as models, and finally infrastructure for serving models in production. The platform is particularly known for training, validation, visualization, and deployment of fresh newly trained models in production continuously relatively quickly. The individual components can share utilities that allow them to communicate and share assets.
The growth of the internet due to social networks such as facebook, twitter, Linkedin, instagram etc. has led to significant users interaction and has empowered users to express their opinions about products, services, events, their preferences among others. It has also provided opportunities to the users to share their wisdom and experiences with each other. The faster development of social networks is causing explosive growth of digital content. It has turned online opinions, blogs, tweets, and posts into a very valuable asset for the corporates to get insights from the data and plan their strategy. Business organizations need to process and study these sentiments to investigate data and to gain business insights(Yadav & Vishwakarma, 2020).
Over the past few decades, software has been the engine of innovation for countless applications. From PCs to mobile phones, well-defined hardware platforms and instruction set architectures (ISA) have enabled many important advancements across vertical markets. The emergence of abundant-data computing is changing the software-hardware balance in a dramatic way. Diverse AI applications in facial recognition, virtual assistance, autonomous vehicles and more are sharing a common feature: They rely on hardware as the core enabler of innovation. Since 2017, the AI hardware market has grown 60-70% annually, and is projected to reach $65 billion by 2025.
Right now, the AI chip market is all about deep learning. Deep learning (DL) is the most successful of machine learning paradigms at making AI applications useful in the real world. The AI chip market today is all about accelerating deep learning (DL) – the acceleration is needed during training and during inferencing. The AI chip market has exploded with players: for a recent research report we counted some 80 startups globally with $10.5 billion spend by investors, competing with some 34 established players. Clearly this is unsustainable, but we need to dissect this market to better understand why it is the way it is now, how it is likely to change, and what it all means.
In 2005, Ray Kurzweil said, "the singularity is near." Now, AI can code in any language, and we're moving to way better AI. GPT-3 got "mindboggling" results by training on a ton of data: Basically the whole Internet. It doesn't need to train on your specific use-case (zero-shot learning). It can fool 88% of people, and we're still in the baby stage.
Mobile devices are popular with hackers because they're designed for quick responses based on minimal contextual information. Verizon's 2020 Data Breach Investigations Report (DBIR) found that hackers are succeeding with integrated email, SMS and link-based attacks across social media aimed at stealing passwords and privileged access credentials. And with a growing number of breaches originating on mobile devices according to Verizon's Mobile Security Index 2020, combined with 83% of all social media visits in the United States are on mobile devices according to Merkle's Digital Marketing Report Q4 2019, applying machine learning to harden mobile threat defense deserves to be on any CISOs' priority list today. Google's use of machine learning to thwart the skyrocketing number of phishing attacks occurring during the Covid-19 pandemic provides insights into the scale of these threats. During a typical week in April of this year, Google's G-Mail Security team saw 18M daily malware and phishing emails related to Covid-19.
Data has been the primary reason why computers & Information Technology evolved. In the modern age Data is the key ingredient that is driving most businesses. The data storage & processing has come of an age. I started my data journey almost two decades ago working on the traditional BI and ETL tools. However over the last few years there has been a major shift from these concepts & technologies.
When opportunity knocks, open the door: No one has taken heed of that adage like Nvidia, which has transformed itself from a company focused on catering to the needs of video gamers to one at the heart of the artificial-intelligence revolution. In 2001, no one predicted that the same processor architecture developed to draw realistic explosions in 3D would be just the thing to power a renaissance in deep learning. But when Nvidia realized that academics were gobbling up its graphics cards, it responded, supporting researchers with the launch of the CUDA parallel computing software framework in 2006. Since then, Nvidia has been a big player in the world of high-end embedded AI applications, where teams of highly trained (and paid) engineers have used its hardware for things like autonomous vehicles. Now the company claims to be making it easy for even hobbyists to use embedded machine learning, with its US $100 Jetson Nano dev kit, which was originally launched in early 2019 and rereleased this March with several upgrades.
Modernization of technology can make a significant impact across many parts of the insurance industry, including underwriting, policy administration, and claims. McKinsey research shows that the potential benefits of modernization include a 40 percent reduction in IT cost, a 40 percent increase in operations productivity, more accurate claims handling, and, in some cases, increased gross written premiums and reduced churn. 1 1. Technology modernization is vital, but--given the significant value at stake and the size of the investment--it should be approached with a healthy dose of caution. Indeed, many insurers miss out on the full benefits of the program for several reasons. First, they don't have a clear view of what sort of actions are needed or the impact such actions could have, which may lead them to undersell both the business value at stake and what is needed to capture it. This approach can enhance the customer experience somewhat, but it doesn't address core challenges such as the ability to reconfigure products quickly or scale users rapidly. is all that is needed, only to find that some capabilities (such as rapid product configurations) require modernization of core systems.
Artificial Intelligence and Blockchain are two common buzzwords that we get to hear these days. While one has already reached a critical point of implementation, the other is an emerging one. While AI offers automation and machine with cognitive intelligence of humans but data capabilities beyond their power, Blockchain is more like a new filing system for digital information, which stores data in an encrypted, distributed ledger format. Through the maintenance of a decentralized database architecture by Blockchain, the record and authentication of certain operations are subject to the agreement of several parties rather than a single authority. This enables the creation of tamper-proof, highly robust databases that can be read and updated only by those with permission.