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."

Artificial intelligence is gaining traction faster than anybody imagined -- quickly knocking down the dominoes of complex tasks that computers have long struggled with. For example, in April this year Wired Magazine ran an article explaining why AI still sucked at transcribing speech. But just six months later, AI is now as good as humans at listening! And there have been similarly dramatic advances in other areas. Adding machine learning to Google Translate has improved error rates by up to 85% since it was introduced a month and a half ago! "It has improved more in one single leap than in 10 years combined" -- Barak Turovsky, product lead for Google Translate The list goes on: Google's AI can already lip read better than people, and the company's image service is now better than people at recognizing and tagging images.

Deep Learning is a technology that is as disruptive as mobile computing or the world wide web that came before it. Yet, most enterprises have no strategy on what to do. This is perplexing given that Deep Learning most hyped up slogan is "The Last Invention of Man". It boils down to one simple fact, enterprises don't understand Deep Learning. To make it even worse, they can't possibly understand the current wave of Artificial Intelligence (AI) developments if they don't understand Deep Learning.

Let's start with understanding "Predictive Analytics". This term originated as an evolution from "Descriptive Analytics", or just plain "Analytics". Descriptive analytics refers to the process of distilling large amounts of data into summary information that is more easily consumed by humans. Example techniques used in Descriptive Analytics include counts and averages to answer a question such as "What were my average sales by region last quarter?" By its nature, descriptive analytics is a backward looking view at "what happened."

Most of us already know that adopting new cloud applications can boost a business's productivity by enabling organizations to be more agile and ready to change course in our fast-moving and connected digital world. But the rapid adoption of cloud apps and services also brings with it profound security threats, including visibility and control challenges that aren't present in traditional on-premises environments. At the same time, the cloud - because of its interconnected, flexible and adaptable nature - can also provide new possibilities for addressing cloud security problems. By leveraging the power of the cloud with a data science and machine learning cloud-based solution, security and risk professionals can solve many of the traditional security challenges found in popular apps like Office 365, Google Drive, Salesforce and Box. In her session at 19th Cloud Expo, Deena Thomchick, Senior Director of Cloud Security at Symantec, detailed how cloud-based data science, machine learning, computational analysis and intelligent algorithms can work together to help to deliver truly intelligent and responsive security and compliance for the cloud.

India's patent laws allow for reverse-engineering of certain technologies. A prime example of this reverse-engineering is in the pharmaceutical space, where Indian pharma companies are allowed to reverse-engineer drugs, especially life-saving ones. These drugs may have been developed by pharma majors in other parts of the world--and then introduced into western markets--after India-based outsourcing firms had helped them out with clinical trials, data gathering and reporting to the US Food and Drug Administration (FDA) or its equivalent to get these drugs passed. Indian courts have continued to allow such reverse-engineering of drugs--famously prompting Bayer AG's then CEO Martin Dekkers to say at a conference a few years ago, "We did not develop this medicine for Indians. We developed it for western patients who can afford it."