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Artificial Intelligence for Business 2020

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A I F O R B U S I N E S S 12 10% 20% 30% 40% 50% 60% ® AUGUST 5-6, 2020 HONG KONG CONVENTION CENTER HONG KONG 1500+ ATTENDEES SEPT 30 - OCT 1, 2020 SANTA CLARA CONVENTION CENTER SILICON VALLEY 6000+ ATTENDEES DECEMBER 9-10, 2020 JAVITS CENTER NEW YORK 5000+ ATTENDEES SEPTEMBER 2-3, 2020 THE ExCeL CENTRE LONDON 17000+ ATTENDEES HEADLINE AI EVENT OF PART OF NOVEMBER 10-12, 2020 INTERNATIONAL CONVENTION CENTER C APE TOWN 15000+ ATTENDEES PART OF SEPT 29 - OCT 1, 2020 SINGAPORE EXPO SINGAPORE 20000+ ATTENDEES PART OF E V E N T S E R I E S WWW.THEAISUMMIT.COM SAVE THE DATE #AISUMMIT PART OF 'Artificial intelligence at the edge will overshadow AI processing in the cloud over the next six to seven years' rtificial intelligence (AI) technolo- gies have already become a part of everyday life for billions of consum- ers across the world, from digital assistants, to smart home devices, to self-parking cars. However, AI holds vastly greater poten- tial, possibly moving beyond such narrow, task-specific applications and into the realm of general intelligence, where machines can perform all activities with equivalent or supe- rior performance compared to human beings. Before realising the vision of general AI, the artificial intelligence industry must tackle some of the biggest challenges fac- ing the business, including security and ethical issues. Solving these problems will require solu- tions providers to work across the entire AI ecosystem, employing not only software, but chip-level hardware solutions that can deliver the required performance and secu- rity to support the next stages of AI technol- ogy development, according to Omdia. The AI business in the past has largely been focused on software. However, to unlock the power and growth potential of AI at the edge in internet of things devices, software providers must work across the AI ecosystem to promote the implementation of AI in chip-level devices. These devices provide not only the processing horse- power required for AI, they also provide built-in hardware security. Current AI models are largely processed on the cloud, in terms of training and infer- ence. A shift is beginning in the consumer devices market and some other markets, such as security cameras and automotive, where hardware and software advances allow for AI model inference to be run on the device itself. Therefore, the emerging narrative for AI processing is that artificial intelligence at the edge will overshadow AI processing in the cloud over the next six to seven years. To accomplish this, AI-enabled devices and products will require sufficient pro- cessing power. These devices are moving AI processing tasks from software to hard- ware, which will require chips with AI-spe- cific enhancements. Almost all suppliers of processor core technology are expected to integrate AI enhancements into their products in the coming years. This will trigger a major increase in AI support in system-on-chip (SOC) integrated circuits, potentially bringing capabilities like voice recogni- tion, face recognition and object recogni- tion to billions of devices. Less than 20 per cent of SOC devices included AI capabilities in 2019. However, with the increasing integration of AI into processor cores, more than half of SOCs will be AI capable by 2023, according to the Omdia Processors Intelligence Service. This rising penetration of chip-level AI will require software vendors to collaborate with companies across the AI ecosystem to ensure the right kinds of capabilities are built into hardware, including security. For example, processor IP supplier ARM is implementing security at the most basic level on semiconductor devices. These security features need to be taken into account at each level, from the chip, to the device, all the way to the cloud. By delivering this intelligence on micro-controllers designed securely from the ground up, ARM is reducing silicon and development costs, and speeding up time to market for product manufactur- ers seeking to enhance digital signal pro- cessing and machine-learning capabilities on-device efficiently. The move to device and edge-level AI processing will also immediately address one of the most serious security issues fac- ing the market, which is the risks involved with sending data to the cloud for process- ing. By conducting AI processing tasks on the chip level, the need to send data through public networks will be reduced. Makers of tablets, smartphones and smart speakers are developing products that use the capabilities of 5G to perform visual AI processing tasks by edge serv- ers and appliances, bypassing the privacy risks involved in sending data to the cloud. By 2025, two out of three smartphones are expected to include built-in AI capa- bilities. Global revenues for AI smart- phones are forecast to increase to $378 bil- lion, up from $29 billion in 2017, according to Omdia. So the AI industry is preparing to move beyond the current stage of nar- row, task-specific solutions and into the era of generalised intelligence. Data scientists are building such AI sys- tems that are constructed based on key data-security principles. With security so intrinsic to AI systems, businesses may need to consider a security vendor at the same time they evaluate an AI provider. However, solving security issues goes hand in hand with addressing larger eth- ical issues related to AI. These ethical issues cannot be solved without address- ing the security challenges and a strong ethical foundation will be essential as AI approaches human or superhuman levels of intelligence. Ethical issues include fundamental questions such as whether an organisa- tion's AI technology provides an overall benefit to society or how much an organi- sation needs to disclose about its AI activi- ties to stakeholders and the general public. One major question for organisations is whether they should be in control of their destiny when it comes to AI data. To answer this question, organisa- tions need to take a look at their practices regarding machine-learning and AI devel- opment. For example, when conducting AI modelling, organisations need to ask themselves whether the training data they are using should comply with privacy laws like the European Union General Data Pro- tection Regulation or US Health Insur- ance Portability and Accountability Act. Alternatively, these companies may decide these laws should only apply to live data. With the age of generalised AI approach- ing, software vendors must work across the AI industry ecosystem to find the solutions for today's security issues and tomorrow's ethical challenges. A Jenalea Howell AI market lead, AI Summit SHARE OF SYS TEM- ON- CHIP DE VICES WITH AI CAPABILIT Y Percentage of global unit shipments Software vendors must work across the AI industry ecosystem to find the solutions for today's security issues and tomorrow's ethical challenges O P I N I O N Omdia 2020 2016 2017 2023 2019 2022 2018 2021 2020

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