We recently held a heated debate at Digital Catapult; would Artificial Intelligence increase the number of jobs? In this blog post, Peter Karney, Head of Product Innovation and Darren Murphy, Digital Communities Manager, go head to head* to explore the big questions surrounding AI. Peter: AI can certainly reduce or eliminate menial activities. One example is a call centre. Currently if you need advice you'll speak to a human being; it's expensive and can be a complete waste of time. But you can put an AI system in that can learn, figure out what you're saying, and do context searches.

Slack's outstanding UX propelled the startup to unicorn status amidst a flurry of competitors, responsive design flourished and gave birth to a new era of mobile friendliness and device agnosticism, and the web as a whole experienced a shift in consciousness as sites became easier to use, apps became more intuitive to navigate, and services became all the more delightful and engaging to interact with. I am proud to say that the field has finally come of age and found itself. At long last, UX Evangelists, Digital Empaths, and Interaction Designers have risen to the highest echelons of the creative class to further the bleeding edge of technology, design, and user delight. With UX Evangelists like Tobias van Schneider, Jennifer Aldrich and Chase Buckley behind the wheel, we are steering towards a brighter future. A future where little big details bring about user delight at every corner, where device agnostic pixel perfection is the norm, and where simple day-to-day experiences engage, excite, and stimulate users in new and innovative ways. So where do you fit into all of this?

Predictive modeling is a formula that transforms a list of input fields or variables into some output of interest. Feature engineering is simply a thoughtful creation of new input fields from existing input fields, either in an automated fashion or manually, with valuable inputs from domain expertise, logical reasoning, or intuition. The new input fields could result in better inferences and insights from data and exponentially increase the performance of predictive models. Feature engineering is one of the most important parts of the data preparation process, where deriving new and meaningful variables takes place. Feature engineering enhances and enriches the ingredients needed for creating a robust model.

The truth is that innovation is really hard. And, by "innovation" I mean real innovation. Not a better mousetrap, but something that the market did not know that it needed, that then becomes adopted (and paid for) in a way in which we could have never imagined our lives without it. So, when it comes to innovation, the thought should be less about what companies are producing innovative products and services, but who - really - is doing the next generation of ideation and exploration. The founder, CEO and CTO of SpaceX, co-founder, CEO and product architect of Tesla, co-founder and chairman of SolarCity, co-chairman of OpenAI and - what many may not even remember - the co-founder of PayPal, is thinking on a whole other level.

Google DeepMind's artificial intelligence AlphaGo made history when it won the complex game of Go against Lee Sedol, one of the greatest world players. As Elon Musk pointed out at the time, experts in the field thought AI was a decade away from reaching that milestone. The momentous event showed that AI was gaining skills typically reserved for humans far faster than we expected. And that very fact could be a problem, Ian Pearson, a futurist with an 85% accuracy track record, told Tech Insider. "You could end up with superhuman machines going down that road," Pearson said.

Last week, I bought a selection of clothes recommended by an online personal stylist: a pair of skinny Topman jeans, a perfectly fitting white T-shirt from Jack & Jones, and most daringly – for me, anyway – some khaki chino shorts by Pull&Bear. We'd carried out the consultation online, with me sharing not only obvious information like my size, desired price range and "daringness" (with "daring" defined as wearing floral shirts or shorts with blazers), but also helping her work out my actual style preferences by telling her brands I like and flicking through endless pictures of well-dressed men to highlight the looks I want. This is no AI horror story, though. My stylist Sophie Bailey-Hine is very real, and her and her colleagues at Thread, a British startup that was founded in 2012, are currently helping 480,000 men find a new image, dress well, or simply sort out their clothes shopping. There is one small twist: Thread has just eight stylists.

Some of society's brightest minds have warned that artificial intelligence (AI) may lead to dangerous unintended consequences, yet leaders of the U.S. intelligence community--with its vast budgets and profound capabilities--have yet to decide who within these organizations is responsible for the ethics of their AI creations. When a new capability is conceived or developed, the intelligence community does not assign anyone responsibility for anticipating how a new AI algorithm may go awry. If scenario-based exercises were conducted, the intelligence community provides no guidelines for deciding when a risk is too great and a system should not be built and assigns no authority to make such decisions. Intelligence agencies use advanced algorithms to interpret the meaning of intercepted communications, identify persons of interest and anticipate major events within troves of data too large for humans to analyze. If artificial intelligence is the ability of computers to create intelligence that humans alone could not have achieved, then the U.S. intelligence community invests in machines with such capabilities.

About NVIDIA NVIDIA (NASDAQ: NVDA) is a computer technology company that has pioneered GPU-accelerated computing. It targets the world's most demanding users -- gamers, designers and scientists -- with products, services and software that power amazing experiences in virtual reality, artificial intelligence, professional visualization and autonomous cars. Certain statements in this press release including, but not limited to, statements as to: the benefits and impact of the NVIDIA Inception Program; NVIDIA's commitment to help companies related to artificial intelligence; and funding through NVIDIA's GPU Ventures Program are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners' products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; as well as other factors detailed from time to time in the reports NVIDIA files with the Securities and Exchange Commission, or SEC, including its Form 10-Q for the fiscal period ended May 1, 2016. Copies of reports filed with the SEC are posted on the company's website and are available from NVIDIA without charge.

Governor Inslee's op-ed for CNBC published July 12, 2016 America's advantage in the knowledge-based economy is our human capital. Well-educated, highly-trained, creative-thinking people are essential to the most innovative companies and successful entrepreneurs in the world today. Skilled people are the currency of economic development for states in the 21st century. Why are nearly 95 percent of all the commercial aircraft in North America built in Washington state? Why is our Puget Sound region the cloud computing capital?

In the beginning, marketing automation platforms grew other channels and tools around their core of email marketing. The basic mode involved if/then rules: if a customer takes this action, show this response. But overlapping campaigns with if/then rules become very complicated very quickly, especially when you're talking about millions of customers, each one in a different frame of mind, and each expecting his/her own personalized experience. As a result, marketing platforms are evolving from their traditional if/then campaigns to the newer approach of customer journeys that are often guided by machine learning. It's the difference between setting up all the rules for the encounter on the one hand, B2B marketing startup YesPath CEO Jason Garoutte told me, and employing something like Netflix's recommendation engine, on the other. "Netflix doesn't write rules about what [movie] you should watch next," he pointed out.