When they established Iguazio<\/strong>, Somekh, Haviv and Segev were already seasoned entrepreneurs and graduates of scientist-supported companies that had recorded commercial success in the high-tech field such as Voltaire and XtremeIO. Somekh, the company’s CEO, says: “If you look at the experience we had acquired, all the previous companies were oriented towards high-performance storage and applications which need to search through vast amounts of data.”<\/p>\n\n\n\n “We were familiar with the way people build data infrastructures and with the problems they contend with. One of the problems we identified was complexity: people attempt to build digital traffic in a completely different manner from the way things were built previously. In the past, data was used more for making business decisions by observing different cross-sections of the system, analyses and reports. The world of IoT has changed that picture however with an infrastructure that also receives data from external sources and is based on external events and information and not just on those in an internal vacuum. Combining these sources with the organization’s historical reports automatically leads to a sequence of business decisions.”<\/p>\n\n\n\n “Our goal was to develop an innovative big-data analysis and management solution that is intended for business and industrial companies and organizations. And indeed, Iguazio’s system, the ICDP (Iguazio Continuous Data Platform) enables us to gather all the information flowing into the organizational system and, using artificial intelligence tools, to conduct large-scale database-like actions on different kinds of databases. Analysis of the figures dramatically enhances organizations’ and companies’ ability to make real-time decisions.”<\/p>\n\n\n\n “From a technical point of view, Iguazio’s software serves as a ‘layer’ above the storage and enables real-time analysis of vast masses of data which constitute a reliable base of up-to-date knowledge for making business decisions. This a revolution in the management, storage and analysis of data. The efficiency of the development is made possible through a combination of big-data and cloud applications.”<\/p>\n\n\n\n ‘We have 90 employees and are already selling the product worldwide \u2013 in Asia, the US and in Europe. We have had two funding rounds: the first of USD 15 million and the second of USD 33 million. Among the investors are the best venture capital funds in the country as well as strategic investors like the American telco company Verizon, the German company Bosch, and others.”<\/p>\n\n\n\n “Let’s look for a moment at cases in the world of IoT that are highly relevant to this topic of productive maintenance in industry: for example, in the case of a client who owns factories with production lines – in the “old” world, a malfunction in one of the machines caused a stoppage of the entire production line. Such a stoppage would incur direct repair expenses and more significant costs of production cessation that hurt revenues. It is important to understand that one of a manufacturer’s biggest fears is a shutdown of the production line.”<\/p>\n\n\n\n “A large part of the market buzz around artificial intelligence is linked to the question of how, the use of IoT, can help prevent these malfunctions. Our solution, with the help of artificial intelligence, enables clients to identify that a malfunction is imminent even before it happens and stops the production line. Using artificial intelligence that analyzes historical events, the system automatically identifies the behavioral pattern which, in the past, meant that a malfunction was about to occur.” <\/p>\n\n\n\n “The world of artificial intelligence is made up of two parts: learning, and decision making based on gathered data, without an obvious connection between the two. Data is gathered from several machines of the same type after which the learning system tries to find a behavioral pattern. For example, if a machine starts heating up, then there is a very high probability that a malfunction will occur. At the end of the learning process, something called a “module” is created. The more data that is gathered over time, the better the modules know how to predict future events based on past occurrences.”<\/p>\n\n\n\n “We run the module that is created on the data gathered from the specific machine and give it greater context. In other words, more data on the machine itself is added to the module: what happened over the last 24 hours, the status of the machine’s other parts etc. For all this to happen, we need a system that will gather the data, with each type of information requiring different data-base capabilities. In the past, this world was simpler, but today it is highly complex because there are so many types of databases.”<\/p>\n\n\n\n “The gathering of data and its transfer to the area in which the learning process takes place, lays the foundation for the next stage – making the decisions in the field. Let’s say that the module predicts a significant malfunction: based on the module we also know how to deal with the problem. For example, the system will automatically change the speed of a certain component’s function so that production will continue while still preventing the malfunction. This is exactly the solution that we have built together with Microsoft and which is based on their cloud.”<\/p>\n\n\n\n “Our system enables receipt of the data, provision of data-base capabilities for these statuses and the performance of historical analyses \u2013 all in a rapid, simple and high-performance manner. It is all done in a closed loop within Iguazio while using cloud technology up to the learning stage \u2013 and application at the end, i.e., the client in the factory.”<\/p>\n\n\n\n “It is important to state that there is certainly room for including human thinking in the decision-making process. The world of artificial intelligence is still in its infancy and humans still possess a wider context. In our example, a human would know that the production line could complete the job within a few hours thereby enabling the repair of the malfunction to be done at greater leisure.”<\/p>\n\n\n\n <\/p>\n\n\n <\/p>\n\n\n\n “A good example of the work we are doing is a client of ours in the automobile field who asked us to develop a system that would predict the life capacity of the vehicle’s battery. His company has a fleet of thousands of vehicles and the possibility of preventing flat battery situations could save them a lot of money. Using our system, data on all the batteries is gathered and their behavioral patterns enable a conclusion to be drawn about which battery is going to die within the next two weeks and for a warning to be issued to the fleet manager.”<\/p>\n\n\n\n “The market of connected vehicles is still evolving so that even such a seemingly simple prediction has significant added value. We are capable of supplying different levels of predictions \u2013 regarding safety for example \u2013 and this has tremendous importance, among others because decisions in autonomous vehicles need to be made more quickly.”<\/p>\n\n\n\n “Artificial intelligence has also become an integral part of the corporate and organizational world in the field of telecommunications. With our help, an Asian mobile operator introduced prediction capabilities into its infrastructure. The company is now able to predict the failure of routers and switches in a specific network within a few hours if it doesn’t make an adjustment to the network. In the cyber worlds, this module is returning in many markets.”<\/p>\n\n\n\n “The world of cyber security is a good example of the extremely important use of data gathering and analysis systems. Certain types of cyber attacks use viruses that appear innocuous, but which can sometimes cause hundreds of thousands or even millions of computers to attack a specific site with the objective of causing it to crash.”<\/p>\n\n\n\n “In an after-the-fact analysis aimed at understanding what happened, previous traffic patterns can help us understand how the attack was created and executed. In hindsight, I can also identify the initial transfer of bots and the beginning of the attack, and to block computers and sites that may repeat the same behavior even before the next attack takes shape. This is a dramatic change in the organizational capability of contending with cyber-attacks.”<\/p>\n\n\n\n “There is currently tremendous ‘hype’ around artificial intelligence. There is no doubt that it has advanced significantly since I studied the topic in the 1990s, but we still have a long way to go to realize its tremendous potential.”<\/p>\n\n\n\n <\/p>\n\n\n\nArtificial Intelligence: Learning and Deciding <\/strong><\/h3>\n\n\n\n
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<\/figure><\/div>\n\n\nPrediction is Protection<\/strong><\/h3>\n\n\n\n
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