Written by Chris Anley, Chief Scientist, NCC Group This paper collects a set of notes and research projects conducted by NCC Group on the topic of the security of Machine Learning (ML) systems. The objective is to provide some industry perspective to the academic community, while collating helpful references for security practitioners, to enable more effective security auditing and security-focused code review of ML systems. Details of specific practical attacks and common security problems are described. Some general background information on the broader subject of ML is also included, mostly for context, to ensure that explanations of attack scenarios are clear, and some notes on frameworks and development processes are provided. This paper collects a set of notes and research projects conducted by NCC Group on the topic of the security of Machine Learning (ML) systems.

As machine learning (ML) systems take a more prominent and central role in contributing to life-impacting decisions, ensuring their trustworthiness and accountability is of utmost importance. Explanations sit at the core of these desirable attributes of a ML system. The emerging field is frequently called ``Explainable AI (XAI)'' or ``Explainable ML.'' The goal of explainable ML is to intuitively explain the predictions of a ML system, while adhering to the needs to various stakeholders. Many explanation techniques were developed with contributions from both academia and industry. However, there are several existing challenges that have not garnered enough interest and serve as roadblocks to widespread adoption of explainable ML. In this short paper, we enumerate challenges in explainable ML from an industry perspective. We hope these challenges will serve as promising future research directions, and would contribute to democratizing explainable ML.

Artificial Intelligence in Medical Imaging Market business report gives explanation about the vital developments in the market which range from the crucial improvements of the market, containing research and development, new item dispatch, pronouncement, coordinated efforts, associations, joint aspire, and territorial development of the key rivals working in the market on a global and local scale. Furthermore, the report also estimates essential market features that comprises of revenue (USD), price (USD), capacity utilization rate, production value, production rate, consumption, import-export, supply-demand analysis, cost, market share, gross margin and market CAGR value. Such a wide range of market parameters make global research report outperforming. An influential Artificial Intelligence in Medical Imaging Market analysis report will give a clear and precise idea to the readers about the overall market to take beneficial decisions. Research studies performed by professional experts in their domains strive hard to make this market report successful.

Doron Gordon is CEO and Founder of Samanage. In the quest for smarter and faster services, IT departments are pioneers in deploying new methods and processes to improve internal service delivery. In the next 12 months, artificial intelligence (AI) will start driving new breakthrough features in service management that will result in unprecedented efficiencies for IT departments and organizations. By moving service management to the cloud, IT teams have shifted from primarily handling break-fix tickets to building comprehensive service catalogs to help empower employees to get work done faster and more effectively. Now, the next wave of disruption is driven by AI and fueled by unprecedented access to data insights that are available thanks to cloud services.

Bruce Milne is Vice President and CMO at Pivot3. Another year ends, another round of predictions begins. Looking back to the widespread adoption of virtualization and blade consolidation of the mid-2000s – which reduced and simplified IT environments and paved the way for converged infrastructure – it's clear we've come a long way. Hyperconverged infrastructure (HCI) hardware-based appliances came next, and have since evolved into more software-defined infrastructure platforms. This has transformed HCI into becoming the platform for supporting hybrid cloud mobility and autonomous workload acceleration.

Tate Cantrell is CTO of Verne Global. Let's break down the acronym ICT: Information and Communication Technology. ICT as a sector and as an industry was created to enable efficient sharing of information. Improved communication between the workers that make up our economies has generated huge returns in terms of GDP growth around the world. No longer is the technology stack enabling information sharing, the technology stack is now creating the information.

Can you imagine taking a cab which is driverless or conducting all basic banking transactions with the help of ATM without requiring a teller. Let's go one step further, your medical treatment being done by a robot or robots teaching you in the classrooms. Well, all of this is not a dream anymore but possible and achievable because of Artificial Intelligence. Artificial Intelligence, though in a nascent stage, has already started to enter a lot of fields and is trying to make a mark. AI has already started to automate some type of jobs and it's not far when AI will replace all the repetitive type of jobs in the analytics space and perform it solely without any human intervention.

Across industries, machine learning drives value – building revenue, cutting costs and making organizations more competitive. But machine learning can be challenging to implement: data scientists are scarce, and many analytics teams lack the right tools for today's big, complex and fast data. Thomas W. Dinsmore is a consultant and author who specializes in machine learning; he has contributed to analytic projects for more than 500 clients around the world.