A eureka moment struck, recalls Mr. Rippel. "Hey, hold on, we could do this for real." The two Ph.D.s quickly decided to try in reality what HBO's fictitious startup was trying on screen. Ever since, the pair of Facebook Inc. alums-turned-startup founders have been living a case of life imitating art imitating life. HBO's comedy series, which began its run in 2014, tells the story of scrappy startup Pied Piper, which hits on a brilliant algorithm for compressing information so it can zip through the pipes of the internet more quickly.

Researchers say hackers could weaponize artificial intelligence to conceal and accelerate cyberattacks, and potentially escalate their damage. Scientists warn that hackers could weaponize artificial intelligence (AI) to conceal and accelerate cyberattacks and potentially escalate their damage. IBM researchers last month demonstrated "DeepLocker" AI-powered malware designed to hide its damaging payload until it reaches a specific victim, identifying its target with indicators like facial- and voice-recognition and geolocation. IBM's Marc Stoecklin said with DeepLocker, "AI becomes the decision maker to determine when to unlock the malicious behavior." Meanwhile, the Stevens Institute of Technology's Giuseppe Ateniese has investigated the use of generative adversarial networks (GANs), which contain two neural networks that collaborate to deceive safeguards like passwords; he designed a GAN that fed leaked passwords found online into an AI model, to analyze patterns and narrow down likely passwords faster than brute-force attacks.

Article written by Peter Jeffcock. AI, machine learning, and deep learning – these terms overlap and are easily confused, so let's start with some short definitions. AI means getting a computer to mimic human behavior in some way. Machine learning is a subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications. Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems.

As the industry moves towards no-code artificial intelligence model training platforms, AI is being positioned as a tool for the masses. It, therefore, comes as no surprise to find that the industry bellwethers are releasing no-code or low-code platforms to build custom machine learning models that can be used with ease and security. Joining the big tech giants is China's internet major Baidu that launched an AI platform designed to make building custom ML models easier and it rules out the need for algorithmic programming. Known as EZDL, the service platform enables developers to build custom ML models with a drag-and-drop interface, Yongkang Xie, tech lead of Baidu EZDL, said in a company statement. He further emphasised developers can build deep learning models which are specific to their business needs only in four steps.

Artificial Intelligence (AI) is changing the way law is being practiced. One of the areas where AI, and more specifically Machine Learning (ML) has been making great strides recently is contract review. The progress is not even limited to reviewing contracts: automated contract generation, negotiation, e-signing and management are fast becoming a reality. Using AI for contracts is the result of an ongoing evolution. Ever since lawyers started using word processors, they have tried to automate the process of creating contracts.

Is it as bad as all that? There's a lot of anxiety out there about automation based on AI making professions obsolete. The fears range from noting specific examples, such as automated trucks and truck drivers, to speculation that entire industries will start mass layoffs and replace people with AI tech. The argument is often, "this time, automation is different." That in the past, when machines made work easier, they created more new jobs to offset the loss of old ones.

Technology is revolutionizing the work we do and how we do it. Increasingly, artificial intelligence (AI) and robots are taking over menial and repetitive tasks, leaving humans to concentrate on work that requires critical thinking. But as machines become better at imitating human intelligence, they're beginning to do more and more thinking for us.

Data scientists have hundreds of probability distributions from which to choose. Data science, whatever it may be, remains a big deal. "A data scientist is better at statistics than any software engineer," you may overhear a pundit say, at your local tech get-togethers and hackathons. The applied mathematicians have their revenge, because statistics hasn't been this talked-about since the roaring 20s. They have their own legitimizing Venn diagram of which people don't make fun. Suddenly it's you, the engineer, left out of the chat about confidence intervals instead of tutting at the analysts who have never heard of the Apache Bikeshed project for distributed comment formatting.

As ever, investors are always looking for the next big thing. In the last couple of years, there has been a considerable amount of interest in bitcoin. The virtual currency has increased significantly in value, but has also been exceptionally volatile. Some investors may have generated high returns from buying the cryptocurrency, while others are likely to have lost money after its decline from almost $20,000 in December 2017 to around $5,500 today. However, with bitcoin seeming to lack real-world potential in terms of its application, its appeal could be relatively limited over the long term.

So far, we've seen a continued uptick in augmented reality, video content, and influencer marketing. But now that 2018 is one-third of the way over, you may find yourself wondering: "what does 2019 have in store for us?" We can't be sure yet, but we have a few predictions for the upcoming New Year. We'll discuss our predictions in this article and give you a look at how the ever-changing digital marketing landscape may look in just a few months. This one may be strange to see on a digital marketing trends list, but hear (pun intended) us out.