In a poll conducted by Integral Ad Science (IAS) 69.0% of agency executives said that ad fraud was the biggest hindrance to ad budget growth, compared with more than half (52.6%) of brand professionals who said the same. How much is ad fraud costing advertisers? Nobody knows, but with estimates ranging from $6.5 billion to $19 billion, there's a lot at stake. Marketers are becoming more assertive in their demands for better fraud prevention measures and they are seeking to increase their knowledge of different fraud types – from bots to unauthorised domain reselling – and wider technology adoptions to drive their Marketing strategies overall. Ad tech providers will need to adapt their technology and techniques to meet this demand.

In a poll conducted by Integral Ad Science (IAS) 69.0% of agency executives said that ad fraud was the biggest hindrance to ad budget growth, compared with more than half (52.6%) of brand professionals who said the same. How much is ad fraud costing advertisers? Nobody knows, but with estimates ranging from $6.5 billion to $19 billion, there's a lot at stake. Marketers are becoming more assertive in their demands for better fraud prevention measures and they are seeking to increase their knowledge of different fraud types – from bots to unauthorised domain reselling – and wider technology adoptions to drive their Marketing strategies overall. Ad tech providers will need to adapt their technology and techniques to meet this demand.

X-rays of arms and legs are among the most frequent diagnosis processes used by NHS Scotland, with around 5,000 procedures annually. Although injuries in these areas are often categorised as minor, misdiagnosis and mismanagement can hamper recovery and lead to financial cost. However, the use of artificial intelligence (AI) and machine learning could help create systems that prevent misdiagnosis. Find out more about the SBRI and how it works. The competition will explore how AI and machine learning can be used to support limb radiographs in the diagnosis of fractures.

Google AI researchers working with Northwestern Medicine created an AI model capable of detecting lung cancer from screening tests better than human radiologists with an average of eight years experience. When analyzing a single CT scan, the model detected cancer 5% more often on average than a group of six human experts and was 11% more likely to reduce false positives. Humans and AI achieved similar results when radiologists were able to view prior CT scans. When it came to predicting the risk of cancer two years after a screening, the model was able to find cancer 9.5% more often compared to estimated radiologist performance laid out in the National Lung Screening Test (NLST) study. Detailed in research published today in Nature Medicine, the end-to-end deep learning model was used to predict whether a patient has lung cancer, generating a patient lung cancer malignancy risk score and identifying the location of the malignant tissue in the lungs.

CHICAGO --- Deep learning - a form of artificial intelligence - was able to detect malignant lung nodules on low-dose chest computed tomography (LDCT) scans with a performance meeting or exceeding that of expert radiologists, reports a new study from Google and Northwestern Medicine. This deep-learning system provides an automated image evaluation system to enhance the accuracy of early lung cancer diagnosis that could lead to earlier treatment. The deep-learning system was compared against radiologists on LDCTs for patients, some of whom had biopsy confirmed cancer within a year. In most comparisons, the model performed at or better than radiologists. Deep learning is a technique that teaches computers to learn by example.

CHICAGO --- Deep learning - a form of artificial intelligence - was able to detect malignant lung nodules on low-dose chest computed tomography (LDCT) scans with a performance meeting or exceeding that of expert radiologists, reports a new study from Google and Northwestern Medicine. This deep-learning system provides an automated image evaluation system to enhance the accuracy of early lung cancer diagnosis that could lead to earlier treatment. The deep-learning system was compared against radiologists on LDCTs for patients, some of whom had biopsy confirmed cancer within a year. In most comparisons, the model performed at or better than radiologists. Deep learning is a technique that teaches computers to learn by example.

In the blink of an eye, we've seen robots begin to take over the workplace: robots for packing and shipping boxes at Amazon, robots for hospital care, robots for dentists, first responders, truck drivers, battle fields, and office buildings. New American writer Dennis Behreandt highlights in the AI print issue that: "Government too, is beginning to benefit from AI, naturally enough at the expense of citizen privacy." Through fingerprint identification and facial recognition, the U.S. federal government has been unaccountably collecting massive databases of your private information, turning AI into a very dangerous powerful aspect in today's world. Elon Musk, technology entrepreneur, investor, and engineer, expressed his concerns with AI at a tech conference in Texas: "I am really quite close, I am very close, to the cutting edge in AI and it scares ... me. It's capable of vastly more than almost anyone knows and the rate of improvement is exponential …and mark my words, AI is far more dangerous than nukes. With Stephen Hawking, Apple CEO Tim Cook, and Oxford's Nick Bostrom agreeing with Mr. Musk and expressing similar concerns, one might question humankind's invention. Mr. Behreandt does the same in the cover story: If we are already having difficulty understanding the limited AI of the present, how can we hope to understand, much less control, the increasingly intelligent AI of the near future? And should we create machine intelligence that exceeds our own, as ours exceeds that of the cockroach? One also should start to wonder, by replicating how the brain works through technology are we attempting to replace God? Christianity Today comments on this in their article: "Does'The Image of God' Extend to Robots, Too?" saying that mere morality isn't enough. Such complicated, uneasy relationships with AI are and will continue to be built on our flawed nature as creators. There is a real danger that humans-as-creators will be selfish and amoral creators, fashioning intelligent designs that exist simply to serve our own interests and desires –or our own sense of right and wrong. The immorality we have wrought on our world will be magnified by AI. Since the fall of mankind, the world has always been influenced by sin. As God's children we naturally want to create, but instead of creating in our own image, we should create in the image of God. By striving for the virtues of morality instead of our own desires, only then will we live in a free and prosperous society. So as technology seems to fly into a new dimension, let's remember the wise words of John Adams: "Our Constitution was made for a moral and religious people.

In this special guest feature, Glen Rabie, CEO of Yellowfin, believes that while many analysts may fear they will be replaced by automation and AI, the role of the data analyst will increase in significance to the business and breadth of skills required. Yellowfin is an Analytics and Business Intelligence software company focused on helping businesses understand their data. Rabie is passionate about data and improving business performance through analytics. Prior to starting Yellowfin, he worked in various roles at National Australia Bank including senior e-business consultant and global manager of employee self-service. Rabie holds a Masters in Commerce from the University of Melbourne.

Researchers from our Adsorption and Advanced Materials Group have used machine learning techniques to accurately predict the mechanical properties of metal organic frameworks (MOFs), materials which could be used to extract water from the air in the desert, store dangerous gases or power hydrogen-based cars. The researchers used their algorithm to predict the properties of more than 3000 existing MOFs, as well as MOFs which are yet to be synthesised in the laboratory. The results, published in the inaugural edition of the Cell Press journal Matter, could be used to significantly speed up the way materials are characterised and designed at the molecular scale. MOFs are self-assembling 3D compounds made of metallic and organic atoms connected together. Like plastics, they are highly versatile, and can be customised into millions of different combinations.

Linear algebra concepts are key for understanding and creating machine learning algorithms, especially as applied to deep learning and neural networks.