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) …
Event-driven architecture (EDA) is a software development paradigm in which the application is laid out as a series of commands, events, and reactions. This is in contrast to a database-centric approach, where incoming data is stored in a database and then called upon later for further analysis. An event, in this case, is any situation of interest that is generated with the use of Internet of Things (IoT) sensors, user-driven interfaces, camera and object recognition systems, and many more sensory mechanisms in the modern enterprise. By switching to an event-driven approach, organizations can undergo digital transformation much easier, incorporating new technologies such as artificial intelligence, digital twins, edge computing, and more into new or already existing applications.
Deep learning is one of the most advanced forms of machine learning, and is showing new developments in many industries. In this article, we'll explain the concept and give some examples of the latest and greatest ways it's being used. There have been many attempts at creating a definition of deep learning. As we've explained in the past, machine learning can be considered as a sort of offspring of artificial intelligence. In the same way, you can view deep learning as a further evaluated type of machine learning.
This article looks at the unique challenges introduced by Edge computing for AI/ML workloads, which can have a negative impact on results. It applies available machine learning models to real-world Edge datasets, to show how these challenges can be overcome, while preserving accuracy in the dynamic nature of Edge environments. The field of machine learning has experienced an explosion of innovation over the past 10 years. Although its roots date back more than 70 years when Alan Turing devised the Turing Test, it has not matured significantly until recently. Two primary contributing factors are the exponential growth in both compute power and data that can be used for training. There is now enough data and compute power (some in specialized hardware like GPUs/FPGAs) that new, real-world problems are being solved every day with machine learning.
Artificial Intelligence is quickly becoming one of the quintessential industries today and reshaping the world as we know it. It has proven its worth in different areas of work with an undeniable impact on the market. Siri to Alexa and self-driving cars to manufacturing-robots are just a few examples of what AI companies have achieved. Tech giants like Amazon, Google, Microsoft and Apple are putting their resources in Artificial Intelligence and are running the race of becoming the biggest artificial intelligence companies in the world. Organisations like NASA are now using artificial intelligence to make themselves even more efficient, a report says. Technical edge is the key to most of the businesses today. As big players are putting everything they have got to get that technical edge, small players may find it overwhelming, if not unfair, to compete with them. Apart from these giants, there are several AI companies which have shown the potential of changing the world and solve the possible disparity some companies may experience due to their technical prowess.
Milton Security, a global leader in Cybersecurity who offers true Extended Detection & Response (XDR) and Managed Detection & Response (MDR) announced the release of the Milton Argos Platform 2.0 (MAP). MAP uses the best of Artificial Intelligence/Machine Learning and human expertise. "We have grown to call this'AI Assisted Threat Hunting'," said James McMurry, CEO and Founder of Milton Security. "This truly hybrid approach with Milton's cutting edge AI & ML technology supporting our operations, allows our Threat Hunters to be the best at what they are uniquely qualified for: detect, deter and mitigate threats in real time," said McMurry. "In response to the growing security skills gap and attacker trends, extended detection and response (XDR) tools, machine learning (ML), and automation capability are emerging to improve security operations productivity and detection accuracy."
AWS Lambda was released back in 2014, becoming a game-changing technology. By adopting Lambda, many developers have found a new way to build micro-services that could be easily achieved. It comes with many additional advantages such as event-based programming, cloud-native deployment, and the development of the now well-known infrastructure-as-code paradigm. A paradigm-shifting technology like AWS Lambda had to define its own standards to support all the modern app development lifecycle requirements. To make things easy to develop, Lambda decided to offer the easiest way of code project management: the zip file format.
Last week, Google released "Underspecification Presents Challenges for Credibility in Modern Machine Learning", a paper that has been sending shockwaves through the Machine Learning community. The paper highlights a particularly thorny problem: even if machine learning models pass tests equally well, they don't perform equally well in the real world. The bugbears of models failing to meet testing performance in the real world have long been known, but this work is the first to publicly prove and name underspecification as a cause. However, before we talk about handling underspecification, we need to describe how machine learning models are put together, and what the problem is. This process has a core tenet that good performance on the testing sample means good performance on real-world data, barring systematic changes between testing and the real-world (called data shift or bias); for instance a model forecasting clothing sales after three months of winter learning is likely to struggle come summertime, having learned a lot about coats but very little about shorts.
In a world filled with technology and artificial intelligence, it is becoming increasingly harder to distinguish between what is real and what is fake. Look at these two pictures below. Can you tell which one is a real-life photograph and which one is created by artificial intelligence? The crazy thing is that both of these images are actually fake, created by NVIDIA's new hyperrealistic face generator, which uses an algorithmic architecture called a generative adversarial network (GANs). Researching more into GANs and their applications in today's society, I found that they can be used everywhere, from text to image generation to even predicting the next frame in a video!
The rapid growth of digitization has successfully paved the way for emerging technologies that lead to better user experience. We are into the fast-paced life cycle where users want everything at the super-fast speed, especially when it comes to accessing the mobile applications. Some survey reports have discovered that the users uninstall 77% of the apps in just three days after downloading. The studies have revealed that the average speed of apps is not as per the expectation level of the users, and this is one of the major reasons for abandoning the application. Undoubtedly developing a mobile app has become an urgent need of an hour for businesses.
Bengaluru, NFAPost: Capgemini announced today its third set of Intelligent Industry offerings: Data Driven Research & Development for Life Sciences. By aligning the expertise of its life science specialists, data scientists and data engineers, Capgemini's latest offer brings the power of Data and Artificial Intelligence (AI) at scale to the research and development (R&D) function. This new set of services will help biopharma companies to improve drug discovery and clinical trials. "Now more than ever there is intense pressure on research and development functions within life sciences to deliver better products more cheaply, quickly and with less risk," comments Franck Greverie, Chief Portfolio Officer at Capgemini and Group Executive Board member. "Artificial intelligence can analyze a broader body of knowledge, clinical data and literature about drugs and conditions at a speed unimaginable for human researchers. Capgemini's new set of Data-Driven R&D for Life Sciences offerings helps to harness the fast-growing set of tools and techniques of digital platforms, modern AI, data science and data engineering to apply them to datasets across a much wider frame of reference than ever before, helping pharmaceutical and biotech companies to reduce the time and cost of getting new therapies to market, and deliver greater personalized therapeutics and patient centricity."