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) …
Deep learning is fueling breakthroughs in everything from consumer mobile apps to image recognition. Yet running deep learning-based AI models poses many challenges. One of the most difficult roadblocks is the time it takes to train the models. The need to crunch lots of data and the computational complexity of building deep learning-based AI models also slows down the progress in accuracy and the practicality of deploying deep learning at scale. It's the training times -- often measured in days, sometimes weeks -- that slow down implementation.
From the Russian President Putin to the Facebook CEO Mark Zuckerberg, everybody is talking about artificial intelligence. Yes, AI is promising a gamut of benefits to forward-thinking businesses. This has been grabbing the attention of everyone who is in the business world. If I tell the definition of artificial intelligence in a single line, "AI is a method of building human intelligence in machines". So machines can think like human, work like human (faster than human), without requiring human intervention.
To get the most out of their data, successful companies are not focusing on queries and data lakes, they are actively integrating analytics into their operations with a data-first application development approach. Real-time adjustments to improve revenues, reduce costs, or mitigate risk rely on applications that minimize latency on a variety of data sources. In his session at @BigDataExpo, Jack Norris, Senior Vice President, Data and Applications at MapR Technologies, reviewed best practices to show how companies develop, deploy, and dynamically update these applications and how this data-first approach is fundamentally different from traditional applications. He covered examples of how leading companies have identified ways to simplify data streams in a publish-and-subscribe framework (for example, how focusing on a stream of electronic medical records simplified the deployment of real-time applications for hospitals, clinics, and insurance companies). He also detailed how a data-first approach can lead to rapid deployment of additional real-time applications as well as centralize and simplify many data management and administration tasks.
Working in the IT field for almost 20 years has afforded me the ability to focus on many different areas and to gain a wealth of experience in many generalities. I would say that I have never been a specialist in anything, other than perhaps network performance monitoring and managing large-scale enterprise software packages. This is not an extremely difficult task, in and of itself, so I have always been interested in gaining some new experience that I can specialize in. The new role I find myself in is engineering, but from a sales slant. That means that I spend more time cultivating relationships and less time on the keyboard, so I spend much of my personal time and most of my work time doing research and actively developing in an effort not to lose my edge.
If you're not using deep learning already, you should be. That was the message from legendary Google engineer Jeff Dean at the end of his keynote earlier this year at a conference on web search and data mining. Dean was referring to the rapid increase in machine learning algorithms' accuracy, driven by recent progress in deep learning, and the still untapped potential of these improved algorithms to change the world we live in and the products we build. But breakthroughs in deep learning aren't the only reason this is a big moment for machine learning. Just as important is that over the last five years, machine learning has become far more accessible to nonexperts, opening up access to a vast group of people.
Machine learning is a set of artificial intelligence methods aimed at creating a universal approach to solving similar problems. Machine learning is incorporated into many modern applications that we often use in everyday life such asSiri, Shazam, etc. This article is a great guide for machine learning and includes tips on how to use machine learning in mobile apps. Machine learning is based on the implementation of artificial neural networks, which are actively used both in applications for everyday life (for example, those that recognize human speech) and in scientific software. These allow for conducting diagnostic tests or exploring various biological and synthetic materials.
Google Lens was announced at the Google I/O 2017 in May and has been slowly gaining steam since then. The app has the ability to identify songs and now can also recognize objects in a smartphone camera's field of vision. Google seems to have taken a page from Samsung's book -- the feature is pretty similar to the company's Bixby Vision, which was launched in August. Both applications use augmented reality algorithms to detect objects in a smartphone camera's range of vision. However, Samsung's execution of Bixby Vision has been flawed at best.
To learn more about applying data science to your business, check out the machine learning sessions at the Strata Data Conference in San Jose, March 5-8, 2018. The promises of AI are great, but taking the steps to build and implement AI within an organization is challenging. Core to addressing these challenges is building an effective AI platform strategy--just as Facebook did with FBLearner Flow and Uber did with Michelangelo. Often, this task is easier said than done. Navigating the process of building a platform bears complexities of its own, particularly since the definition of "platform" is broad and inconclusive.
In our daily lives we are all faced with the need to make decisions. We usually make personal decisions based on personal experience and information. We draw from our friends' experience as well as Internet and other external sources. The data needed to solve a particular challenge usually fits in our head and is structured in sketches in our mind, a notebook or a special document such as mind map. In business, the volume of available data increases in multiples and we are tasked with data collection, analysis and the sheer impossibility of not only holding all the information in our head but even structuring it in a single document.
Industry 4.0 is impacting not only Operational Technology, but Information Technology as well. This can most readily be seen, perhaps, when one considers how machine learning and artificial intelligence is driving efficiencies in business processes that begin with physical documents, digitize them, and then classify, enrich and dispatch them to workflows before they are, finally, archived in document management systems. "Digital" is now firmly embedded in every business. But even with technology as an integral part of the organization and its strategy, it is people who will ensure success in a world that continues to reinvent itself at an unprecedented rate. Simply adding more technology to the enterprise is insufficient; we must focus instead on enabling people to do more with that technology.