From chatbots to predictive analytics, IT leaders will share how they can generate business insight and new application scenarios through artificial intelligence and machine learning. There is no technology that is hotter than AI and machine learning. At the moment, some companies are using these technologies to mimic human thinking to attract customers and support business operations. This trend will receive more attention in the next few years, according to Gartner's data, by 2020, more than 30% of CIOs will make artificial intelligence and machine learning one of the top five investment priorities. Initially people were worried that robots would replace everyone's jobs, but now this trend has eased somewhat. Because humans and machines work more likely. Accenture CTO and chief innovation officer Paul said that companies are no longer interested in re-employing employees in highly automated positions. He has recently co-authored a book on the impact of artificial intelligence on the global workforce - "Human + Machine: Reconstructing Work in the Age of Artificial Intelligence." In the course of investigating 1,500 companies, 65% of CEOs admitted that their employees and CIOs have not yet prepared for artificial intelligence. Only 3% of executives said they have increased their training to cope with the changes brought about by artificial intelligence. So this is a huge gap, and CEOs must quickly accept change and tell employees what they should do to cope with this change. But no matter what the outcome, the era of automation through AI, machine learning and robotics has arrived. CIOs are learning, experimenting, and building new business applications in artificial intelligence and machine learning. Emerging digital technologies are changing the way workers work, even in Accenture. They have automated about 23,000 jobs and redeployed their employees. What's even more surprising is that Accenture's 450,000 employees will be reassigned to work roles at any time, and they need to regain new skills. To help employees make this transition, Accenture developed a machine learning application that scans resumes and predicts when an employee's work will become less important. This app will combine the work experience of employees to score the potential unnecessaryness of their position. For example, because of AI and automated applications, an employee's skills will be out of date within 18 months. Paul believes that this application is more than just a "end of the world" prophet for employees. It can also consider the overall quality and ideas of employees and recommend the skills they may need in the future to keep their jobs. Key point: CIOs need to take on the company's artificial intelligence strategy and actively work with key stakeholders in human resources and other business units to ensure strategic consensus. CIOs must also quickly identify and eliminate their prejudice in AI awareness because responsible organizations can quickly integrate AI into the enterprise. Machine learning is a core component of many international companies' digital strategies. Sunil, a CIO of Lennox, which has a market value of $4 billion, says they use the Spark software in the database to analyze the information flow of the company's commercial heating and air conditioning systems and monitor the machines in real time. Performance, which can predict when a machine will fail and quickly plan ahead. “Databricks (a technology company in the Spark field) can support us to use this data and predict the operation of the equipment with 90% accuracy. In the past we often made predictions and contacted the air conditioner dealers. But this is usually false. The alarm is very annoying," CIO said. Before choosing Databricks, Lennox investigated a number of analysis tools, but each analysis tool was used to handle a single instance. Databricks provides a unified platform on which to manage hundreds of megabytes of data in hundreds of databases, and it runs on Microsoft Azure, so Lennox does not have to maintain the system. With Databricks, CIO Sunil's team worked with business people to build a data flow model. Based on sparks software, they can easily extract value from data analysis. The key point: When manual predictions bring great risks to business development, CIOs must decisively introduce new tools. Just as Sunil has mastered 10 billion pieces of data, he is at a loss. But when he actively used the analysis software to discover the mysteries of the data, he also got amazing results. David is the CIO of American Express Global Business Travel. In the early days of his tenure, robotic process automation (RPA) and machine learning techniques were implemented to improve the efficiency of corporate travel service providers in their business. Now he has used RPA to automate the cancellation of the ticket and refund process. At the same time, David also created a new machine learning algorithm that helps customers find better airfare and hotel accommodation by searching for prices within the industry. This was done manually by employees. It can be said that this technology has increased customer satisfaction and created more business revenue. David said that in his last job, he also used machine learning to scan for financial fraud, and now it is also using this technology to greatly improve the business. The key point: CIO is usually cautious about automated processing and machine learning, because no one has enough confidence in new things. But CIOs must clearly and decisively build influence in the business, using technology and automated processes to solve business problems, because they are the real promoters of technology business. Adobe CIO Cynthia is using machine learning to help analyze the failure trends of Adobe systems and then automatically fix the problems to avoid more serious consequences. The idea is that if there is a tool that can self-discover the possibility of an outage, then it can actively eliminate or mitigate the damage caused by this problem. This tool is named HaaS, Health-as-Service. The tool captures and corrects errors, such as integration with Adobe ERP failures, or attempts to access the wrong data sources of the company's various analysis systems. Cynthia believes that HaaS has reduced the manual operation's 30-minute duration to 1 minute, which has saved Adobe 330 hours to solve the problem. With detailed problem reports, Adobe engineers can create permanent fixes. Automated monitoring and repair is a huge plus for Adobe. The work was carried out based on the machine learning diagnostic test framework created in 2017. It is reported that Adobe's business uses artificial intelligence. As early as the end of 2016, the company launched Sensei, the creator of the world's best digital experience. Through a unified framework of AI and machine learning, developers can create and publish documents on the cloud platform and can analyze and track the performance of web and mobile applications to drive innovation. Key point: Using machine learning to identify patterns is the key to creating self-healing capabilities. CIOs need to make the system self-healing without human intervention. Hearst Business Media, their core business assets include the drug database software First Databank and the credit rating Fitch RaTIngs. And they are using machine learning to connect to the medical device database, making it easier for customers to access information. Enterprise SVP and CIO Mark said they are developing their own machine learning algorithms and using Google's TensorFlow machine learning software to model the company's datasets. He believes that companies tend to use open source tools to deal with business problems. Key point: Adopting a scalable approach is the key to Hearst's successful use of machine learning. One of the reasons that machine learning is very practical from a business perspective is that it can handle different databases in a common way. For example, credit rating Fitch RaTIngs products need to understand many business entities, and First Databank needs to understand how drugs are used all over the world. Putnam Investments, a company that provides fund and institutional investment strategies, believes that AI and machine learning are key to driving financial services company analysts to increase the success rate of stock investments. Its CIO Mehta said analysts work closely with corporate data scientists to write papers to help them gather information from large amounts of data. The company is also researching related algorithms to improve the recommendation accuracy of sales. This is a huge change for them to come to book and drive the efficiency and productivity of the entire business drive. At the beginning of the AI ​​and machine learning strategy, the CIO formed a portfolio of software engineers, data scientists, analysts, and suppliers, and created an excellent data science center. And these business partners have begun to embrace change to achieve better automation recommendations. He believes that artificial intelligence and machine learning are part of the digital transformation of the enterprise, which requires cloud computing to achieve IT infrastructure leadership and create a separate platform to run the business. The key point: in terms of AI landing, companies should step by step, set expectations appropriately, and take into account the new problems caused by this, as well as actively seek answers. In the field of artificial intelligence, it is not a sudden algorithm that produces insight. Under the leadership of software vendor CDO Ashok, Intuit is accelerating the development of artificial intelligence and machine learning. They are using Amazon Web Services to help their QuickBooks chat assistant robots better understand and handle natural language and build their own roles in Verizon's big data platform. More and more processing will guide users through the classification of hundreds of Quickbooks. CIOs say they are dealing with more than one billion QuickBooks transactions and can accurately categorize them because they are using AWS's machine learning and cloud technologies to scale. The company's TurboTax system uses artificial intelligence to help users get the most tax refunds, which saves users up to 40% of tax preparation time and the task of retrieving documents. Key point: Ashok, who worked at NASA's Ames Research Center, believes that developing sound algorithms requires attracting the right engineering talent to solve real business challenges. They are currently hiring engineers to ensure that business goals can be achieved through artificial intelligence. As the CIO of Riverbed Technologies, Rich faces unique challenges. As we all know, Riverbed is a software vendor designed to improve the performance of wide area networks. They are testing how to use machine learning to get data from multiple supply chains in a company for better business insight. "We want to process more data than ever before through machine learning," Rich said. For example, Riverbed can combine order management and ERP data with historical data on other factors such as weather to find patterns that predict future performance. They want to more accurately predict the risks downstream of the supply chain, including capacity and ability to meet customer orders. In addition, they use machine learning to automatically tune performance configurations and discover network security risks. Rich hopes to create a single data lake and get more business insight from it. The key point: good strategies also need to be treated cautiously for artificial intelligence and machine learning. Rich is carefully evaluating tools and technologies, including IBM Watson, to address business risks. Like many large banks, Bank of America also collects a large amount of customer data. But they also have difficulty getting actionable advice from these data. Bill, the chief analyst at Bank of America, is working to change that. In the past few months, he has been using Salesforce.com's Einstein AI/machine learning technology to enhance personalized services in the bank's small business, wholesale, commercial wealth and commercial banking sectors. For example, if a customer searches for information about a mortgage on a Bank of America website, the customer service agent can track the customer the next time the customer visits. For example, the software can advise agents to call potential customers between 10 am and 2 pm on Thursday, because they are more likely to answer the phone by calculating. Einstein can also place an invitation calendar on the agent's calendar reminding them to call potential customers next Thursday. These capabilities will be at the heart of many financial services organizations' efforts. Cultivate a 360-degree view of the customer so they can accurately recommend relevant services. Bill believes that we are moving from a descriptive world to an unspeakable world. The core value of this world is to stay one step ahead and expect customer needs and our channels to be interconnected. Key points: Companies and CIOs must be patient and testable about artificial intelligence and machine learning. But be prepared to extend the running algorithm as well. Always putting customers at the center is the only law of technology realization. Ed is the president of operations and technology at MasterCard. He believes that artificial intelligence and machine learning span all aspects of Mastercard. They are using these algorithms to eliminate repetitive manual work, giving employees time to do more creative and value work. At the same time, MasterCard uses machine learning tools to enhance change management in its product and service ecosystem. For example, machine learning tools help determine which changes are risk free and which changes require additional review. In addition, MasterCard is using machine learning to detect anomalies in its system, indicating that hackers are trying to gain access. Ed also established a "safety net" on the network. When it finds suspicious behavior, it cuts off the circuit breaker that protects the network. Ed said: "We have a fraud scoring system. Update it by constantly checking the transaction and rate the next transaction to be made." Key point: AI/ML is just one of the tools in the extensive toolkit for payment processors. Despite so many shiny new tools on the market, Ed said CIOs shouldn't expect them to solve business problems magically. Summary: The application of the scene has always been a controversial place for artificial intelligence. The challenge now facing machine learning is how to make machines smarter. All of our applications today, whether it is image, sound or image recognition, or a language translated into another language, and testing, are some of the features of artificial intelligence. CIOs need to combine artificial technology to bring artificial intelligence to the ground, because applications in many scenarios have begun to mature, and it is too late to pay attention. Small Power Diesel Generator,Small Power Diesel Generator Set,Small Power Generator,Air Cooled Portable Diesel Generaor Shanghai Kosta Electric Co., Ltd. , https://www.ksdpower.com
