Our technical report provides an overview of the relevant parts of an ML lifecycle--selecting the right problem, the right data, and the right math and summarizing the model output for consumption--as well as questions that relate to those areas of focus. As the federally funded research and development center (FFRDC) known for AI engineering, and with its long experience in cybersecurity, the SEI has the expertise to advise you--the decision makers adopting these tools--on evaluating the adequacy of ML tools applied to cybersecurity. To that end, we structured the report around the questions you should ask about ML tools. We chose this framing, rather than proposing a detailed guide of how to build an ML system in cybersecurity, because we want to enable you to learn what a good tool looks like. When decision makers have difficulty identifying a good tool, the market will usually stop providing them.
The Department of Trade and Industry (DTI) sets the wheels in motion for the crafting of the country's artificial-intelligence sector road map as testament to the Philippine potential as an AI powerhouse in the Asean region. Trade Undersecretary Rafaelita M. Aldaba of the Competitiveness and Innovation Group led the formal signing of an agreement with distinguished data scientists, Dr. Christopher P. Monterola and Dr. Erika Fille T. Legara, for the formulation of an AI road map early this month. With the advent of the Fourth Industrial Revolution (4IR) where technology becomes more enmeshed with everyday life, AI advancement is seen as one of the key factors to help keep our country competitive. AI's importance is underscored as it is emerging to be a potential bright spot for our country with wide opportunities for growth for our competent work force. "The formulation of the AI road map is very important and timely. This effort provides the impetus that will move the country forward to keep up with the rapidly changing times," Aldaba emphasized.
For the running example in Figure 1, this abstraction would replace the application-specific identifiers triangle and EQUILATERAL with generic placeholders, such as VAR1 and VAR2. After this abstraction, both approaches use an RNN-based sequence-to-sequence network that predicts how to modify the abstracted code. Given the increasing interest in learning-based approaches toward software engineering problems, we will likely see more progress on learning-based repair in the coming years. Key challenges toward effective solutions include finding an appropriate representation of source code changes and obtaining large amounts of high-quality human patches as training data.
Since the advent of computing, people have lauded technology's potential to act as a human brain. In truth, computers have worked nothing at all like a brain for most of their history. In recent years, though, they've been getting closer. In a new paper in Nature,"Towards Spike-based Machine Intelligence with Neuromorphic Computing," Priyadarshini Panda and her co-authors Kaushik Roy and Akhilesh Jaiswal, both at Purdue University, provide an overview of the computer's long and ongoing road to achieving something akin to the thinking power of the brain. Panda, assistant professor of electrical engineering, emphasizes that there's still nothing that acts like a brain - not least in part because much of how the brain works is still a mystery.
Model explainability is one of the most important problems in machine learning today. It's often the case that certain "black box" models such as deep neural networks are deployed to production and are running critical systems from everything in your workplace security cameras to your smartphone. It's a scary thought that not even the developers of these algorithms understand why exactly the algorithms make the decisions they do -- or even worse, how to prevent an adversary from exploiting them. While there are many challenges facing the designer of a "black box" algorithm, it's not completely hopeless. There are actually many different ways to illuminate the decisions a model makes.
The rapid evolution of technology is both exciting and daunting, as businesses strive to identify and back the right technologies that will drive positive change within their organisation. Understanding which to explore and test and which to press on and implement can be challenging, particularly when new technologies are frequently announced with much fanfare far before benefits are truly realised. To help get an expert perspective on the matter, Savannah Group recently hosted an event for a selection of CIOs, CTOs and CDOs and invited Dr Jai Menon, Chief Scientist at Cloudistics to lead a discussion on the major technological changes driving change in organisations at the moment. Jai's distinguished career has seen him serve as CTO of some of the largest systems businesses in the world including IBM and Dell. He is recognised as an IBM Fellow and was a pioneer behind the creation of RAID technology – now a $20 billion industry.
The growth of ecommerce in the recent past can only be described as explosive and sweeping across the planet. According to a 2016 study, half of all dollars spent online in America belong to Amazon. And consider this, Recommendation Engines alone drive 35% of that revenue. But it is not ecommerce alone that's reaping the huge benefits that recommendation engines have to offer. Direct to device streaming services such as Netflix, Spotify among others, analyze user behavior almost to a micro moment level, then gather data surrounding similar users who are likely to buy the same items based on their browsing history, and provide that much needed nudge to move on to the next purchase on the platform.
This complimentary Business Briefing is a 120-minute session designed to bring leaders together to learn about, reflect on and discuss recent research and the key competencies for organizational agility in the context of the massive changes that are anticipated from the widespread implementation of artificial intelligence (AI). Today organizations need to gather and act on information, make decisions quickly and implement them to meet the rapidly-evolving requirements of customers and the business environment. The ability to do so is becoming increasingly important in this era of digital transformation and advances in Artificial Intelligence (AI). We provide a framework for leaders, addressing important considerations for those who want to approach building agility within their organization in a focused, deliberate way.
Deep Learning is a subset of Artificial Intelligence that falls under the umbrella of machine learning. It is inspired by the structure and function of the human brain. Deep Learning is a method of enabling computers to carry out specific tasks that require human intelligence without any human intervention. The human brain consists of a complex web of neurons connected together, this network of neurons (also called neural network) is responsible for complex functionalities of the human brain. Deep Learning was built on the core idea of mimicking the complex process of human brain to enable machines to think on their own i.e. machines with artificial intelligence.