Artificial Intelligence: A View of 2017

Artificial Intelligence: A View of 2017

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With clear benefits across industries, Artificial Intelligence and smarter machines are set to revolutionize our world. 

Let’s roll-up our sleeves and look beyond conversations, vendor pitches and industry speeches to really define what Artificial Intelligence (AI) and Cognitive Computing means for businesses. Are businesses ready? Where will they invest and how will they build these innovative solutions? What benefits will result? While studies reflect growing interest in AI, there’s little actual use at this time.

Google, Facebook, Amazon, IBM and Microsoft are joining forces to create a new AI partnership dedicated to advancing public understanding of the sector, as well as coming up with standards for future AI endeavors.

AI is becoming a key enabler of digital transformation for businesses as they manage the complexity of constant change. Here are the dynamics that will shape the future of AI in 2017:

Artificial Intelligence and Machine Learning

One of the building blocks of AI is machine learning. Machine learning includes technologies such as deep learning, neural networks and natural-language processing and encompasses more advanced systems that understand, learn, predict and adapt without being explicitly programmed, thereby operating autonomously. Machine learning has been helping create these intelligent systems that go beyond simply executing predefined instructions. What machine learning is good at is helping us simultaneously work with multiple types of data — text, images, live sensor inputs, media and video feeds. More importantly, it is really good at recognizing patterns within these data and relating them to each other in ways that would be very hard to program. This leads to untold benefits in automation of how data is analyzed and provided to decision makers.

Banking

In banking, AI and machine learning techniques can model current real-time transactions, and deliver predictive models of transactions based on attributes such as their likelihood of being fraudulent. Today, banks need to spot fraudulent and money laundering activities before the money moves. By leveraging AI and worldwide databases of addresses, names across cultures and geographies, banks can accurately find, link and visualize customer information. Machine learning can further be applied to connect relations between different customers, timelines of when and where the transactions took place, and eventually graphs of money use and cash flow patterns. With enhanced visualization capabilities, investigators can save money, improve detection and streamline investigations better than ever before.

Healthcare

There is a new trend in advanced healthcare called ‘precision medicine’ and it is based on data, algorithms, and precision molecular tools. Precision medicine changes the focus of health and medical efforts from identifying symptoms to understanding and treating the mechanisms of disease. A machine learning approach can simultaneously take into account many different parameter sets such as considering past patterns, intensity of present outbreaks and social network graphs of people contacts and other environmental and social determinants of health, like your postal code.

Supplier management and customer service

Chatbots are changing the face of customer service for many online businesses. IPsoft’s AI platform, Amelia automates work knowledge and is able to speak to customers in more than 20 languages. Amelia has been trained to understand and respond to helpline queries in a quick and efficient manner and has been applied to use across industries.

Supply chain, logistics and warehousing

As artificial intelligence applications are increasingly being used to manage the movement of domestic and international goods, there is going to be a fundamental shift in the logistics function. We are going to see greater optimization of capabilities and innovation in the sector with items like self-driven delivery vans, unmanned aerial vehicles becoming a reality. There will be increased machine learning adaptation in supply chain models in 2017, which will help businesses differentiate and drive revenue. Use of AI in supply chains is helping businesses innovate rapidly by reducing time to market; and helps them evolve by establishing an agile supply chain capable of foreseeing and managing uncertainties. Machine learning may also improve business models by providing dynamic information about inventory and movements to supply chain operators. Tracking packets or goods shipments at an individual level removes the need to work with gross statistics. It also gives businesses the power to work with very small packets and very large shipments simultaneously to optimize supply chains in near real-time.

In addition, AI supports new product development and innovation in companies. Some of these innovations are part of popular culture, such as wearable tech where emotion sensing and measuring physiological data helps brands understand how a customer feels about their product.

At Pitney Bowes, the role of AI and machine learning is one that makes a real difference in the lives of online shoppers every day. Two-third of consumers say they will back out of online transactions when shipping costs are too high. Machine learning algorithms enable Pitney Bowes to accurately calculate the lowest possible shipping cost along with taxes and duties for some of the world’s largest retailers. As the amount of data increases with each calculation, the accuracy is only going to get better and better.

With clear benefits across industries, AI and smarter machines are set to revolutionize our world. Companies that are ready to adopt AI in their processes will be a step ahead of their competitors today and in the future.