The continuing march of technology had quite an impact on chatbots this past year, with machine learning taking the fore. The ability to deploy a chatbot that can not only answer FAQs and redirect relevant queries to live operators, but that can actually learn new answers to as-yet unasked questions--that's what we mean by ground-breaking. A spate of new sites came into their own this year that proved chatbots don't have to be chatty to be incredibly successful. Using a conversational interface on a database search tool, for example, allows users to enter semantic search phrases, and have the results parsed for relevance before being returned in similarly universal language. These sites and apps prove that the AI-powered software, with a user-friendly interface on the front-end, is a winning combination (we'll have examples to share below).
When managers make strategic decisions, an important question that needs to be addressed is why and how their clients are satisfied. The corresponding answers need to be included in decision-making processes to increase user satisfaction. Clients' written comments can be a useful source to achieve this objective. However, this strategy is too broad to incorporate all the factors influencing clients' opinions. Artificial Intelligence (AI) has proven to be one of the most efficient resources to extract key results from vast amounts of data.
During the British summer, conversations about sport become almost ubiquitous. This year, however, one participant in those conversations was very different: IBM Watson, IBM's cognitive intelligence. The All England Lawn Tennis Club knew that 2016 would feature unusually fierce competition for attention, with the Tour de France and Euro 2016 taking place alongside Wimbledon. More than ever before, social media was going to be a vital tool in directing that conversation, and directing attention to SW19. Wimbledon's "Cognitive Command Centre" – powered by Watson's intelligence running on a hybrid, IBM-managed cloud - scanned social media for emerging news and trends.
IT IS a nice coincidence that IBM's greatest boss and Sherlock Holmes's sidekick shared a surname. But whether it was Thomas J. or Dr John H. who inspired the name of the firm's latest venture into artificial intelligence (AI), the association of that name with a touch of genius makes "Watson" a clever choice. This sense of cleverness was reinforced in 2011, when Watson won a specially staged version of an American TV quiz show called "Jeopardy!" The system's capacity to parse questions posed to it in the show's convoluted, pun-ridden English, to search huge natural-language databases for clues, to synthesise those clues into answers and to frame those answers in a conversational way was able to beat to the draw the finest minds of American quizdom. Winning game-show prizes, though, is not a good enough business model to justify the investment it takes to build such a system.
A good deep learning model has a carefully carved architecture. It needs enormous training data, effective hardware, skilled developers, and a vast amount of time to train and hyper-tune the model to achieve satisfactory performance. Therefore, building a deep learning model from scratch and training is practically impossible for every deep learning task. Here comes the power of Transfer Learning. Transfer Learning is the approach of making use of an already trained model for a related task.
Just like the invention of steam power in 1780, electricity in 1870, computers in 1960, AI changes our world today. Although it has been a while since AI reached our doorstep, the potential it has to offer is huge. So how artificial intelligence is changing business today? AI is good at processing large amounts of data. For businesses, it opens new horizons for quick and well-considered decision-making, risk management, forecasting, logistics optimization, marketing personalization, etc.
Machine learning has advanced from the age of science fiction to a major component of modern enterprises, especially as businesses across almost all sectors use various machine learning technologies. As an example, the healthcare industry is utilizing machine learning business applications to achieve more accurate diagnoses and provide better treatment to their patients. Retailers also use machine learning to send the right goods and products to the right stores before it is out of stock. Medical researchers are also not left out when it comes to using machine learning as many introduce newer and more effective medicines with the help of this technology. Many use cases are emerging from all sectors as machine learning is being implemented in logistics, manufacturing, hospitality, travel and tourism, energy, and utilities.
Microsoft said on Monday it would buy artificial intelligence and speech technology firm Nuance Communications for about $16 billion (€13.43 billion) in cash, as it builds out its cloud strategy for healthcare. The deal comes as both companies, which partnered in 2019 to automate clinical administrative work such as documentation, gain from a boom in telehealth services with medical consultations shifting online due to the Covid-19 pandemic. "Nuance provides the AI layer at the healthcare point of delivery," Microsoft chief executive Satya Nadella said in a statement, adding "AI is technology's most important priority, and healthcare is its most urgent application." Microsoft's offer of $56 per share represents a premium of 22.86 per cent to Nuance's last close. Shares of Nuance rose nearly 23 per cent in pre-market trading.
Are you thinking of learning programming languages like C, Python or R to work on machine learning projects? AutoML could save you all the time and effort. Lately, Automated machine learning or AutoML has become a popular solution to build computer vision systems. The tech communities are awash with conversations around AutoML as to how it will change the way machine learning is done with limited or no coding knowledge. From autonomous vehicles to handwritten text recognition, face recognition, personalised recommendations, and diagnosing from x-ray images, computer vision is transforming industries globally.