The first wave of artificial intelligence was targeted at helping companies to automated data collection. The second wave is focused on extracting meaningful value from data, through assessing data patterns. According to an article in Venture Beat, the focus of many companies with artificial intelligence platforms established is to use machine learning and predictive analytics to extract meaning from captured data. Through such meaningful analysis, this has the potential to open up new business models, products, and services. Be specific, work on the design and unify teamsThere are three elements to this approach.

During 2017 it was hard to escape predictions that artificial intelligence is about to change the world. In 2018, this is unlikely to change. However, an increased focus on repeatable and quantifiable results is likely to ground some of the "big picture" thinking in reality. Don't get me wrong – in 2018 AI and machine learning will still be making headlines, and there are likely to be more sensationalized claims about robots wanting to take our jobs or even destroy us. However, stories about real innovation and progress should start to receive more prominence as the promise of the smart, learning machines increasingly begins to bear fruit.

For as long as labor has existed, there has been a fear that technology will kill jobs. Scribes were pissed at the printing press. Lumberjacks and weavers thought life was over after the creation of mechanical axes and looms. Yet, life and labor have gone on. Not only has life continued, it's thrived.

While most of the attention for how artificial intelligence (AI), machine learning and big data can impact companies is focused on the business to consumer (B2C) space, business to business (B2B) companies need to pay attention or they risk their future success. Customers have the same expectations for a simple and easy buying experience whether it's a B2C or B2B interaction. So, if you're in the B2B space, I hope your organization is beginning to explore and plan for, if not already implemented, big data and machine learning to your operations. Since the buying cycle for B2B is usually significantly longer and more complex than B2C (Gartner found there is an average of 5.4 people on corporate buying teams), I would argue that it's even more important for B2Bs to use machines in any way possible to quickly get to know customers and respond to decision-makers with the objective of closing sales as efficiently as possible. According to Salesforce's 2016 Connected Customer report, by 2020, 57% of business buyers will depend on companies to anticipate their needs and if they don't, business buyers will have no problem switching brands.

Smartphone cameras are pretty incredible things to have in your pocket, and the Pixel 2 does a very good job of making every image look fantastic. But you can't do better than a big, full-frame DSLR – the trouble is, they're not very pocket-friendly. So, if you're fed up of your phone taking washed out, shallow photos, this AI is designed to take your old smartphone pictures and give them DSLR-like quality – even if your smartphone isn't all that snazzy. Known as WESPE (Weakly Supervised Photo Enhancer), the team of data scientists behind the project aim to bring DSLR-like qualities to smartphone cameras. The idea is that, by training a deep learning system using photos of the same scene taken with a phone camera and on a DSLR, it'll learn the difference and automatically make those adjustments on images it's never seen before.

Google's DeepMind Artificial Intelligence AlphaGo Zero recently attained an important milestone--the Artificial Intelligence (AI) taught itself how to play the strategy game Go without any human interaction and was able to beat the world's best Go players. The ability to reach this level of performance with human input is a significant step forward in the maturation of AI. Over the past several years, AI has made significant progress in a wide variety of areas such as image and speech recognition, drug discovery, and algorithmic trading. In most of these cases, the AI relies on vast existing data sets and some degree of human engagement. A long-standing ambition of AI researchers has been to create algorithms that do not rely on already existing data sets nor the need for human input.

What are the first things that come to mind when someone says the words Artificial Intelligence? A technology that will cause job losses, large-scale economic upheaval and global conflicts, and which poses a threat to the role of humanity in the world? If your main reading is the mainstream media, you'd be excused for thinking so. The coverage in the media has largely focused on the impact on livelihood (jobs and the future of work) and shows a growing negative sentiment (see Figure 1). Note: Sentiment (volume of positive/negative social media messages) as measured through headlines on Artificial Intelligence and related topics from top media publications in the mentioned time period.

We produce 2.5 exabytes of data every day. Every minute of every day 3.2 billion global internet users continue to feed the data banks with 9,722 pins on Pinterest, 347,222 tweets, 4.2 million Facebook likes plus ALL the other data we create by taking pictures and videos, saving documents, opening accounts and more. We are at the limits of the data processing power of traditional computers and the data just keeps growing. While Moore's Law, which predicts the number of transistors on integrated circuits will double every two years, proved remarkably resilient since the term was coined in 1965, those transistors are now as small as we can make them with existing technology. That's why there's a race from the biggest leaders in the industry to be the first to launch a viable quantum computer that would be exponentially more powerful than today's computers to process all the data we generate every single day and solve increasingly complex problems.

Imagine the perfect personal assistant. This partner would understand your needs -- often before you've even expressed them -- and know exactly how to deliver what you're asking for. They would make helpful suggestions without becoming intrusive, and keep you from missing appointments and opportunities. Most importantly, this personal assistant would be someone you can trust implicitly. Now, how do you embody those traits in an artificial intelligence-powered service?

Virtual agents and chatbots will be the top consumer applications of artificial intelligence over the next five years, according to a consensus poll released today by TechEmergence, a marketing research firm for AI and machine learning. The emphasis on virtual agents and chatbots is in many ways not surprising. After all, the tech industry's 800-pound gorillas have all made big bets: Apple with Siri, Amazon with Alexa, Facebook with M and Messenger, Google with Google Assistant, Microsoft with Cortana and Tay. However, the poll's data also suggests that chatbots may soon be viewed as a horizontal enabling technology for many industries. "The most unexpected result was that so many founders who were not directly involved in the chatbot space or smart home/device space were very excited about these areas," wrote Daniel Faggella, founder of TechEmergence, in an email interview.