Software can make "decisions" when specified criteria are satisfied (for example, "buy" and "sell" decisions); and humans can use AI to help improve the quality of their own decision-making. Unlike other software, however, AI can make decisions autonomously without any human involvement. A decision to adopt AI can raise fundamental ethical and moral issues for society. As legal responsibility is a subset of moral (or ethical) responsibility, for AI to gain acceptance and be trusted in a given sector, a business will need to take into account the ethical considerations and the legal factors that flow from them.
At Borisov's company Progress, he uses the example of chatbots they built for hospitals, which "automate the process for patients to book doctor appointments by talking to a chatbot." These chatbots are powered by Artificial Intelligence and Natural Language Understanding, he says, meaning that "It is trained to understand different intents or conversations. "The reduction of staff for repetitive processes requiring customer support employees is the biggest promise of chatbots in the long-term," says Borisov. Many basic chatbots are built using "functional programming," Borisov explains: "Developers today can use Natural Language Processing (NLP) algorithms to extract structured data from natural language, and use this information to create more intelligent chatbots.
"According to a recent survey of the general public by AXA Insurance, 2% of respondents openly admitted to filing a fraudulent or exaggerated whiplash claim, and 11% knew someone who had done the same. These fraudulent insurance claims cost insurers millions, in turn raising premiums for the consumer. At the start of the year, market research firm Forrester predicted that 500,000 IOT devices would suffer a breach in 2017. Business Insider estimates that annual cyber insurance premiums will more than double over the next four years, growing from to $8 billion in 2020.
In his view, businesses are best off hiring a leader with deep knowledge of the field who can help build up an organization's knowledge and capabilities in a centralized way. Ng likened the need for centralized AI talent to the rush for mobile talent earlier in the century. When iOS and Android were new, it was harder for businesses to hire people with strong expertise developing mobile applications. As a result, businesses had to build centralized teams for building their mobile apps.
James Waterhouse is head of insight & data science at Sky Betting and Gaming. Tim Bickley, team leader in the Ocado Technology data science team, says while a large proportion boast a mathematical background, the flavour of qualifications is less important than strong maths skills, a proven track record of independent research and problem solving, and solid programming skills. Wael Elrifai, senior director of Enterprise Solutions at Pentaho and the company's AI and Machine Learning expert, is currently building a team of more than 20 engineers and data scientists. Having recognised that PhDs or Master's degrees in machine learning are virtually non-existent, Pentaho has turned to training company Pivigo, which specialises in turning PhDs and MScs into Data Scientists and bridging the skills from traditional STEM degree areas to data science, machine learning and AI.
As the Internet of Things (IoT) and Artificial Intelligence (AI) grow and expand, the way companies and industries doing business and the way customer responds to the market have been changing swiftly. The way industries and customer-oriented companies are doing business using Internet of Things (IoT) and Artificial Intelligence (AI), they have come to the conclusion that AI and IoT will design and define the future and will create a trend of success or failure. According to some estimates, spending on the Healthcare IoT solutions will reach $1 trillion within one decade and will reach the stage for highly personalized, accessible, and on-time Healthcare services for everyone. The companies have access to massive customer data from their various interactions with online apps and websites are in a stage of earning millions of dollars for what they have in hand, the data.
AI is already helping engineering companies model new jet engine designs, oil companies predict where to drill for oil, drug companies identify promising new areas for research. Data analytics allowed companies to identify interesting patterns in data which could help them better target customers and understand operations – transforming online sales and marketing, and well understood production processes. Next is the AI infrastructure layer, which allows developers to build AI tools such as machine learning and neural nets using existing frameworks. Companies which identify a problem that needs solving; understand the context, find the right data, apply the right intelligence and build the right solutions with the right tools will be the ones who bring about the next big disruption.
Automated security systems now apply AI techniques to massive databases of security logs, building baseline behavioural models for different days and times of the week; if particular activity strays too far from this norm, it can be instantly flagged, investigated, and actioned in real time. This has led firms like IBM, Amazon Web Services, Microsoft Azure, Unisys and startups like BigML, Ersatz and DataRobot and to offer machine learning as a service (MLaaS), providing API-based access to the core libraries necessary to apply machine learning techniques to large data sets. In the short term, however, AI is still on a short leash within many security environments: a recent Carbon Black survey of 410 cybersecurity researchers found that 74 percent still see AI-driven cybersecurity solutions as flawed and 70 percent said they can be bypassed by attackers. Over time, tools will become more sophisticated and ever-larger security data sets help learning algorithms add ever more nuance to their detection mechanisms.
Bruce Sinclair's The IoT Inc Business Show podcast has a great interview in his Episode 64: IIoT Manufacturing From the Shop Floor to the Top Floor with Tanja Rueckert of SAP. IIoT or connected manufacturing is literally "connecting" Operational Technology (OT) and Informational Technology (IT), meaning production and core business processes have end-to-end connectivity or what is known as visibility from the shop floor to the top floor. The focus has been primarily on the first aspect, that is, connectivity and merging the operational technology with informational technology and analyzing the business processes in between. The connectivity, or as Tanja calls it, from shop floor to top floor is hitting the nail on the head -- having the operator's visibility extended to maintenance professionals where usage patterns and trends are analyzed to head off issues before they become costly problems or extending the life of the machine.
He'll oversee a core team of more than 30 AI, robotic process automation (RPA) and machine learning specialists. However, history is littered with examples of technology rapidly advancing, just look at what's happened in the utilities and transportation sectors. As enterprises strive to digitally transform, many are turning to AI to enhance the power of process automation. Think about RPA as being highly effective at data capture where business process automation handles moving the data once captured to other systems.