Of all the "What to watch" lists gumming up your social media feeds, "Which tech trends are on the horizon" should be the one you pause long enough to read. It's important to recognize what technology is increasingly in popularity so you can allocate the time and funds to make these trends pay off for your business. Heading into the new year I tackled some of the top trends in Digital Transformation along with some key trends in Customer Experience. However, with 2017 CES upon us (and by no means just a consumer show any longer) and a new year off to a fast start, we must not ignore the tech trends that will shape the enterprise in the year ahead. Here are my takes on what to spend and save on for enterprise technology in 2017. From technology budgets to knowing how tech will change business, tech trends matter for businesses big and small.

So here is my post-Christmas/pre-New Year Bulletin with some predictions about what's going to happen in 2017 ... enjoy. "To truly realise transformative value of machine-learning and digital intelligence platforms throughout your organisation, you need to understand how it intersects with both your organisational chart and your legacy tech. How would true automation evolve the current roles on my team? Do we have the right skill set to build new strategies to take advantage of the multiplying effect of thousands of experiments and tests?" "In 2017 I foresee progressive marketers using artificial intelligence (AI) capabilities to translate data science into powerful campaigns. When AI combines with machine learning and automation, data collection is enhanced and deeper analysis enables marketers to create and execute campaigns that automatically optimise timings, content, and channels."

Cleaning and preparing data is a critical first step in any machine learning project. In this blog post, Dataquest student Daniel Osei's takes us through examining a dataset, selecting columns for features, exploring the data visually and then encoding the features for machine learning. This post is based on a Dataquest'Monthly Challenge', where our students are given a free-form task to complete. After first reading about Machine Learning on Quora in 2015, Daniel became excited at the prospect of an area that could combine his love of Mathematics and Programming. After reading this article on how to learn data science, Daniel started following the steps, eventually joining Dataquest to learn Data Science with us in in April 2016. We'd like to thank Daniel for his hard work, and generously letting us publish this post.

There has been no shortage of promises recently about how deeply the application of machine-driven artificial intelligence is expected to change our society in the next decade. Microsoft announced its ambition to conquer cancer using natural language processing to analyze research papers in close to real time. Google--now a self-proclaimed AI-first company--will make computers sound just like humans by applying machine learning to a vast corpus of human voice samples. Facebook is using AI to analyze satellite footage to locate all human life and fulfill its promise to bring the internet to the world's entire population. And the list goes on.

The age of artificial intelligence (AI) is upon us. In the past few years, vast improvements have been made in how well computers can recognise objects in images and understand human voices. Progress in these areas has been made due to increased computing power and the availability of large stores of data, which, when combined, have made AI systems dramatically more effective. These same forces are also being used in marketing. AI, or'cognitive', marketing systems use industrial computing power, big data, and machine learning to improve marketing performance.

In preparation for the new year, PerformanceIN continues its annual tradition of connecting with performance marketing experts to get their single biggest prediction for the industry in 2017. In this piece, Steven Ledgerwood, MD at Emarsys, sees artificial intelligence being a big hit among marketers, starting from next year. Today's customer has a massive amount of choice when shopping. However, they are increasingly busy and constantly on the move, which makes it difficult for brands to stand out in an ocean of available products and offers. How can marketers create personalised offers for their customers in 2017, especially when considering the current explosion of shopping channels and enormous amounts of customer data?

It seems every 2017 Event Trends list I read features Chatbots and Artificial Intelligence (AI). Having followed emerging technology trends over the last year, I think it's likely these five trends are responsible: Trend #1 -- Era of Mobile: Increasingly our population has become mobile-centric. Think about it…when was the last time you went anywhere without your smartphone? Trend #2 -- App Fatigue: While we are spending more time on our smartphones, we are downloading fewer and fewer apps. Trend #3 -- Rise of Messaging Apps: In the last two years, companies have focused on establishing a presence on social networks like Facebook, Twitter, Pinterest and Instagram; however, messaging apps including WhatsApp, Facebook Messenger, WeChat and Kik have far surpassed those social networks in terms of the number of active users per month.

With new paradigm and technology, there are a variety of misconceptions. However, I will try to solve the mystery about bots Centurysoft is a service-provider that will help you differentiates myth from facts about bots. Contrary to the expectation of many, bots do not use AI currently, and some of them will never use AI. Bots use a natural language understanding that matches what people say in their actual intent. For instance, there are various ways to say that you want to book a ticket for a movie.

New York City's entire taxi fleet -- nearly 13,250 vehicles -- could be replaced by just 3,000 ridesharing cars if these services were optimized, according to a new study from the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT). CSAIL's Daniela Rus and her team created an algorithm that crunched data from three million New York City taxi rides, calculating routes and schedules for two-person, four-person, and ten-person vehicles. The results showed that 3,000 four-person cabs could help handle 98 percent of the City's demand (with a waiting time of 2.3 minutes), while 3,000 two-person cabs could handle 94 percent and just 2,000 ten-person vehicles could handle 95 percent. "To our knowledge, this is the first time that scientists have been able to experimentally quantify the trade-off between fleet size, capacity, waiting time, travel delay, and operational costs for a range of vehicles, from taxis to vans and shuttles," Rus said in a press release. "What's more, the system is particularly suited to autonomous cars, since it can continuously reroute vehicles based on real-time requests." Many of today's ridesharing systems, like those used by Uber and Lyft, are relatively inflexible when it comes to planning and assigning routes.

Businesses are often data-rich but information-poor. Machine learning is changing that. The use of artificial intelligence to let computers learn independently through algorithms without being explicitly programmed can help companies process vast quantities of complex data to improve analytics, predictive accuracy and decision-making. Machine learning is already being used in everything from fraud detection to self-driving cars, and in sectors from marketing to government. "We are currently working on machine learning to pick up early signals of ill health. My current role is to ensure that this is implemented in line with national recording guidance which does not cover machine learning. This is currently in pilot phase in the A&E in Salford."