Algorithms'learn' from the data they are trained on to create a machine learning model that can perform a given task effectively without needing specific instructions, such as making predictions or accurately classifying images and emails. These techniques are already used widely, for example to identify spam emails, diagnose diseases from X-rays, predict crop yields and will soon drive our cars. While the technology holds enormous potential to positively transform our world, artificial intelligence and machine learning are vulnerable to adversarial attacks, a technique employed to fool machine learning models through the input of malicious data causing them to malfunction. Dr Richard Nock, machine learning group leader at CSIRO's Data61 said that by adding a layer of noise (i.e. an adversary) over an image, attackers can deceive machine learning models into misclassifying the image. "Adversarial attacks have proven capable of tricking a machine learning model into incorrectly labelling a traffic stop sign as speed sign, which could have disastrous effects in the real world. "Our new techniques prevent adversarial attacks using a process similar to vaccination," Dr Nock said. "We implement a weak version of an adversary, such as small modifications or distortion to a collection of images, to create a more'difficult' training data set.

AI is getting smarter, requiring less training data and moving from cloud to Edge. Finnish AI startups gathered last week in Business Finland's Customer Club to share relevant information for intelligent industry and to check the latest state of the art of AI solutions for industrial use. There are plenty of small, young Finnish companies that have created money saving and innovative AI solutions especially for pulp and paper industry, mining companies and oil refineries that are strong businesses in Finland. Possibilities for different profitable applications are numerous with solutions that combine the use of cloud and edge in storing and analyzing data. All data from industrial machines cannot be moved to the cloud because there is typically just too much data or the latency requirements don't allow it.

Check out the "Model Development, Governance, Operations" sessions at the Strata Data Conference in New York, September 23-26, 2019. Best price ends June 28. Our surveys over the past couple of years have shown growing interest in machine learning (ML) among organizations from diverse industries. A few factors are contributing to this strong interest in implementing ML in products and services. First, the machine learning community has conducted groundbreaking research in many areas of interest to companies, and much of this research has been conducted out in the open via preprints and conference presentations.

The vast scope of GDPR has raised fresh challenges -- chief among them is the complex interaction between AI and the GDPR. In particular, this shines a spotlight on Article 22, which concerns automated profiling and decision-making, where the incorrect use of personal data can have huge ramifications for the individuals concerned. The problem is that existing AI system logic takes automated decisions without user consent. Since data is the engine behind AI, Article 22 impacts every industry hoping to leverage the power of technology to drive efficiencies through automated means. In an increasingly data-reliant business landscape, how can organisations reconcile the advent of disruptive technologies and their inherent risks while remaining fully compliant?

New Delhi (CNN Business)Stephen Schwarzman, the billionaire founder of investment firm Blackstone (BX), has given the University of Oxford its largest single donation in hundreds of years to help fund research into the ethics of artificial intelligence. The £150 million ($188 million) contribution will fund an academic institute bearing the investor's name, the British university announced Wednesday. The Stephen A. Schwarzman Centre for the Humanities will bring together all of Oxford's humanities programs under one roof -- including English, history, linguistics, philosophy and theology and religion. It will also house a new Institute for Ethics in AI, which will focus on studying the ethical implications of artificial intelligence and other new technology. The institute is expected to open by 2024.

It's not hard to find a good story in the tech industry. The problem is that due to the industry's staggering gender gap, most of these stories center on the struggles and accomplishments of men. In this article, we aim to provide a platform for female technologists to share the stories of how they got into engineering, the biggest challenges they've faced, and their advice to the next generation of women in tech. You'll meet a former geologist turned product manager, an academic who fell in love with data science, a senior tech leader who discovered her dream job after the first two companies she worked for folded, and more. CCC's technology solutions are designed to increase connectedness among companies in the automotive industry, including insurance carriers, manufacturers, parts suppliers and collision repair shops. Ranjini Vaidyanathan was in academia and earned a PhD before realizing she had a passion for data science. While changing focuses wasn't always easy, Vaidyanathan said the transition was made easier by some simple, yet powerful, advice from her mentors. "When the going gets tough, what'll help you pull through is your passion for the technical work." How did you get into engineering? I studied applied science and mathematics before finally switching to data science after my PhD. It took me some time to decide what, exactly, I wanted to pursue. I had been doing pen-and-paper theory work as a student, but after a certain point, I realized I found applied problems more interesting. What's the biggest challenge you've faced in your career, and how have you worked to overcome it? Switching fields from academia to data science was challenging. I had to brush up industry-relevant skills like programming, and also adjust to the paradigm shift in thinking, both in terms of technical and soft skills.

Artificial intelligence (AI) is already playing a role in combatting malware and other threats. Through machine learning, AI can now do more than just add malware samples to security software. It can also detect future versions and similar variants of the same malware. But what if the very AI that helps organizations fight these threats was co-opted by cybercriminals? What if malware became smarter and tougher and almost undetectable through AI?

Nearly every industry is using artificial intelligence in one way or another to improve business outcomes. AI holds great promise as new and exciting applications are discovered, but there is a catch. There aren't enough trained AI engineers capable of carrying out the work. Karen Roby talks with Sameer Maskey, a professor of AI at Columbia University and founder of Fusemachines, about the shortage and what can be done. The following is an edited transcript of the interview.

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Kevin Benedict serves as the Senior Vice President, Solutions Strategy, at Regalix, a Silicon Valley based company, focused on bringing the best strategies, digital technologies, processes and people together to deliver improved customer experiences, journeys and success through the combination of intelligent solutions, analytics, automation and services. He is a popular writer, speaker and futurist, and in the past 8 years he has taught workshops for large enterprises and government agencies in 18 different countries. He has over 32 years of experience working with strategic enterprise IT solutions and business processes, and he is also a veteran executive working with both solution and services companies. He has written dozens of technology and strategy reports, over a thousand articles, interviewed hundreds of technology experts, and produced videos on the future of digital technologies and their impact on industries.