Imagine a future where the CEO of a manufacturing firm turns to his Chief Innovation Officer and asks: "Bob, how can we innovate on our product and save 10 per cent of production cost to stay competitive against low-cost firms?" So far this seems to be a normal conversation that occurs daily across organisations around the world. Except that'Bob' is really an AI computer program on the CEO's iPhone. A study from Oxford University says that A.I. will replace up to 45 per cent of jobs within 20 years. There is a lot of talk about how such intelligent systems and chatbots will eliminate low level jobs, customer service and repetitive tasks.

Artificial intelligence is a young field full of nearly unlimited potential that remains largely misunderstood by most people. We've come a long way since Watson won Jeopardy in 2011 and IBM formed the business unit with over $1 billion in investments. AI is no longer a one-trick pony. AI technology from IBM Watson and multiple companies such as WayBlazer and SparkCognition has moved firmly into the real world. We have no doubt come a good distance on what is indeed a very long road.

Two of the biggest trends in technology right now are machine learning and artificial intelligence. In fact, the two terms are used almost interchangeably. However, there are subtle but important differences between them both. In many ways, machine learning is a subset of artificial intelligence. Also, the term AI is older than machine learning.

It has become common practice for attackers to use Artificial Intelligence (AI) and Machine Learning (ML) to link tools together so that they can be run in parallel when conducting an attack. Attackers use AI and ML to take the results from one tool and then allow the other tools to "learn" about the finding and use it against other systems. As an example, if a one tool finds a password, that tool can feed the information to another tool or bot that may conduct the exploitation of one or many systems using the discovered password. AI and ML allows for an attacker to program a toolset or bot to act like a "real" attacker. As an example, the tool or bot may launch a phishing attack against an organization and then take the results of the phishing tool and conduct other types of attacks just as a human would.

Science fiction has already explored the theme of robot rights, such as the film Bicentennial Man. Science fiction likes to depict robots as autonomous machines, capable of making their own decisions and often expressing their own personalities. Yet we also tend to think of robots as property, and as lacking the kind of rights that we reserve for people. But if a machine can think, decide and act on its own volition, if it can be harmed or held responsible for its actions, should we stop treating it like property and start treating it more like a person with rights? What if a robot achieves true self-awareness?

A North-East fintech company is using artificial intelligence (AI) and speech recognition software to allow users to discuss their financial accounts with their phone. Darlington-based MyFirmsApp creates custom app platforms for accountants and has recently secured the right to use next generation AI platform, Amazon Lex. The software will allow the company to build apps with'human-like intelligence' that can see, hear, speak, understand and interact with the world around them. The new tech will allow accountants and their customers to have a conversational experience when using their app. CEO Joel Oliver believes that being able to create chat bots using Amazon Lex will help accountants carry out simple tasks and allow them to become more productive.

Data scientists everywhere are delving more deeply into deep learning (DL). If you're only skimming the surface of this trend, you might think that the Spark community, which focuses on broader applications of machine learning, is watching it all from the sidelines. Though Spark is certainly at the forefront of many innovations in machine intelligence, DL industry tools and frameworks--such as TensorFlow and Caffe--seem to be grabbing much of the limelight now. Be that as it may, Spark is playing a significant, growing, and occasionally unsung role in the DL revolution. With all of that in mind, we can track Spark's growing adoption in the DL world of deep neural nets through its incorporation into the following open-source industry initiatives, tools, frameworks, and approaches: Spark for multi-language training of DL models: Deeplearning4j (DL4J) leverages Spark clusters for fast, distributed, in-memory training of DL models that were developed Scala or Java.

Internet trolls, by definition, are disruptive, combative and often unpleasant with their offensive or provocative online posts designed to disturb and upset. The common assumption is that people who troll are different from the rest of us, allowing us to dismiss them and their behavior. But research from Stanford University and Cornell University, published as part of the upcoming 2017 Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2017), suggests otherwise. The research offers evidence that, under the right circumstances, anyone can become a troll. "We wanted to understand why trolling is so prevalent today," said Justin Cheng, a computer science researcher at Stanford and lead author of the paper.

As futuristic as the year 2025 sounds, it is now less than eight years away. Will time travel, flying cars and summer vacations to Mars be in the cards? Probably not, but drone delivery, chatbots and an AI-driven retail industry are near certainties. And the people driving these changes, the "digital native" generations, are quite open to disrupting, if not destroying, old retail practices. Sure, McKinsey predicts brick-and-mortar stores will still account for approximately 85% of U.S. retail sales in 2025, but will the shopper journey be the same as it is today?

Several artificial intelligence projects have been created over the past few years, most of which still had some kinks to work out. For some reason, multiple AI solutions showed signs of racism once they were deployed in a live environment. It is one of the major hurdles to overcome before artificial intelligence services and products can effectively become a part of mainstream society. Although this incident is not racism in its extreme form, it highlighted an underlying problem with AI-driven recognition solutions. After sending a data packet containing nearly 2,000 faces to an AI solution, Chinese researchers discovered their project showed a high degree of bias.