Recently, "The Economists" emphasized on the fact that data has become the most valuable commodity held by people. When small chunks of data are combined on a large scale, then it's termed as Big Data. While we are busy in securing Big Data from attacks, it is quietly contributing towards the growth of Artificial Intelligence. Well, Machine Learning, a section of AI is making exponential improvements and can be termed as "the information escalated strategy." Simply put, huge chunks of data are required to make, test and prepare AI.
For a very long time, women working in the fields of science, technology, engineering and math were unwelcome and underappreciated. Take for example the story of Katherine Johnson and her colleagues, who made remarkable contributions to the early years of NASA's space program. The world had not even heard of her name until two years ago, when the movie, Hidden Figures, hit the screens. Sadly, it is still a man's world in the STEM fields, and women struggle every day to find a strong foothold in it. The disparity between the number of men and women with successful careers in STEM is unfortunately large.
The Blockchain market is forecast to grow in a 61.5% Compound Annual Growth Rate (CAGR) between 2016 and 2021, developing from $.2B to $2.3B in 2021. The largest segments are in the company and financial services and technologies, telecom and media. The biggest protocols comprise Bitcoin, Ethereum, and Ripple. Deloitte discovered that banks have allegedly stored between $8B to12B annually with blockchain technology to enhance operational efficiencies. The Artificial Intelligence (AI) market is predicted to rise from $8B in 2016 to $72B from 2021, reaching a 55.1 percent CAGR.
Big Data includes so many specialized terms that it's hard to know where to begin. Make sure you can talk the talk before you try to walk the walk. Data science can be confusing enough without all of the complicated lingo and jargon. For many, the terms NoSQL, DaaS and Neural Networking instill nothing more than the hesitant thought, "this sounds data-related." It can be difficult to tell a mathematical term from a proper programming language or a dystopian sci-fi world.
If you follow developments in cloud architecture, you may have been hearing a lot recently on the importance of an "intelligent cloud" and an "intelligent edge." Cloud providers who have traditionally focused on providing infrastructure and software have begun to realize that there is only so much value they can drive through these as-a-service offerings, and it is no surprise that the word "cognitive" has begun to creep into more marketing and speechifying on cloud. But it's important for developers and data scientists to be able to distinguish between the marketing and the reality of a truly cognitive cloud. IBM is leading in artificial intelligence, with Watson's deep domain expertise helping clients of every size, across all industries, every day. Watson -- which is available only on the IBM Cloud --has the full range of cognitive technology – ML, AI, cognitive -- because that's what is needed for decision making and transformative business outcomes.
To put it like it is, entrepreneurs now see artificial intelligence as the most effective competitive edge one can install in their business. However initially, not many establishments could afford its cost –but now, thank goodness that AI can be served from the clouds as a service. Yes, the cloud is making sharing of top technologies easier than ever, in fact, the platform has made available amazing computing powers and software possibilities to customers with low financial strength, worldwide. AI software being the most sought of all technologies. IDC puts it clear that going by the current demand in cognitive and AI systems which has raised to 50% according to Compound Annual Growth Rate, spending on these techs will shoot to 57 billion come 2021, from the current state of $12 amount.
Recreating human intelligence in an artificial way has long been the dream of scientists and film directors alike. Cinematic creations like Bladerunner's replicants and Star Wars' C3-PO are the human reproductions that spring to mind when we think about Artificial Intelligence (AI). But the reality is closer to computer-based AI like The Terminator's Skynet. In this blog, we take a look at how these advances in technology are impacting marketing and which companies are using AI to their advantage. As a marketer, you want to be as relevant to your customers as possible in order to market to them as effectively as possible.
IBM (NYSE:IBM) is in the middle of reinventing itself from a hardware company that sells servers to businesses, to a services company that provides platforms, analytics, and cloud computing to its customers. Sure, it still sells hardware, but IBM is continually looking ahead to a world where service revenue dominates its top line. To that end, IBM recently made an announcement about a new artificial intelligence (AI) platform it's launching, called Cloud Private for Data. The name doesn't necessarily roll off the tongue, but IBM says that this new data science and machine learning platform will make it easier for its customers to make data-driven decisions. "Designed to help companies uncover previously unobtainable insights from their data, the platform is also designed to enable users to build and exploit event-driven applications capable of analyzing the torrents of data from things like IoT sensors, online commerce, mobile devices, and more," the company said in a press release.
Over at the Lenovo Blog, Dr. Bhushan Desam writes that the company just updated its LiCO tools to accelerate AI deployment and development for Enterprise and HPC implementations. The newly updated Lenovo Intelligent Computing Orchestration (LiCO) tools are designed to overcome recurring pain points for enterprise customers and others implementing multi-user environments using clusters for both HPC workflows and AI development. LiCO simplifies resource management and makes launching AI training jobs in clusters easy. LiCO currently supports multiple AI frameworks, including TensorFlow, Caffe, Intel Caffe, and MXNet. Additionally, multiple versions of those AI frameworks can easily be maintained and managed using Singularity containers.
Artificial intelligence (AI) holds the promise of transforming both static and dynamic security measures to drastically reduce organizational risk exposure. Turning security policies into operational code is a daunting challenge facing agile DevOps today. In the face of constantly evolving attack tools, building a preventative defense requires a large set of contextual data such as historic actuals as well as predictive analytics and advanced modeling. Even if such feat is accomplished, SecOps still needs a reactive, near real-time response based on live threat intelligence to augment it. While AI is more hype than reality today, machine intelligence -- also referred to as predictive machine learning -- driven by a meta-analysis of large data sets that uses correlations and statistics, provides practical measures to reduce the need for human interference in policy decision-making.