According to Forbes, the artificial intelligence market is growing rapidly. In addition to the discussion and media attention, as well as emerging startups and Internet giants trying to acquire these startups, this area attracts more investment and corporate use. According to a survey conducted by NarraTIve Science, 38% of companies used artificial intelligence last year, and will grow to 62% by 2018. Forrester Research estimates that investment in artificial intelligence will increase by more than 300% year-on-year in 2017. IDC estimates that the size of the artificial intelligence industry will grow from $8 billion in 2016 to more than $47 billion in 2020. The concept of artificial intelligence encompasses a variety of techniques and tools, some of which have been around for a long time, while others have just emerged. To give the outside world a better understanding of current trends, Forrester has released a TechRadar report on artificial intelligence that analyzes the artificial intelligence technologies that 13 companies should focus on. Based on Forrester's analysis, the following are the top 10 artificial intelligence technologies listed by Forbes: 1. Natural language generation: Generate text using computer data. It is currently used in customer service, report generation, and business intelligence information summaries. Example vendors: AtTIvio, Cambridge SemanTIcs, Digital Reasoning, Lucidworks, NarraTIve Science, SAS. 2. Speech recognition: dictating human language and transforming it into a form useful for computer applications. It is currently used in interactive voice response systems and mobile applications. Example vendors: NICE, Nuance Communications, OpenText, Verint Systems. 3. Virtual Assistant: In Forrester's words, this is "the darling of the current media", which includes both simple chat bots and advanced systems that can communicate with humans. It is currently used in customer service and support, as well as in smart home management tools. Example vendors: Amazon, Apple, Artificial Solutions, Assist AI, Creative Virtual, Google (microblogging), IBM, IPsoft, Microsoft, Satisfi. 4. Machine Learning Platform: Provides algorithms, APIs (application programming interfaces), development and training kits, data, and computing power to design, train, and develop computational models into applications, processes, and machines. Currently widely used in enterprise applications, most of them include prediction or classification functions. Example vendors: Amazon, Fractal Analytics, Google, H2O.ai, Microsoft, SAS, Skytree. 5. Artificial intelligence-optimized hardware: GPUs (graphics processing units) and applications designed to run artificial intelligence computing tasks, specifically designed and architected. Currently used to change deep learning applications. Example vendors: Alluvialate, Cray, Google, IBM, Intel, NVIDIA. 6. Decision Management: An engine that inserts rules and logic into the artificial intelligence system for initial setup/training, as well as ongoing maintenance and optimization. This is a proven technology that is used in many different enterprise applications to assist or make automated decisions. Example vendors: Advanced Systems Concepts, Informatica, Maana, Pegasystems, UiPath. 7. Deep Learning Platform: A special form of machine learning platform that includes multiple layers of artificial neural networks. Currently used mainly for pattern recognition and classification based on big data sets. Example vendors: Deep Instinct, Ersatz Labs, Fluid AI, MathWorks, Peltarion, Saffron Technology, Sentient Technologies. 8. Biometrics: Empowering more natural interactions between humans, including but not limited to image and touch recognition, speech and body language. Currently mainly used for market research. Sample providers: 3VR, Affectiva, Agnitio, FaceFirst, Sensory, Synqera, Tahzoo. 9. Machine Processing Automation: Use scripts and other methods to automate human operations to support more efficient business processes. It is currently used for certain tasks and processes where labor costs are high or inefficient. Example vendors: Advanced Systems Concepts, Automation Anywhere, Blue Prism, UiPath, WorkFusion. 10. Text Analysis and Natural Language Processing: Natural language processing techniques use statistical and machine learning methods to understand the structure, meaning, mood, and intent of statements. Currently used for fraud detection and information security, a variety of automated assistants, and the mining of unstructured data. Example vendors: Basis Technology, Coveo, Expert System, Indico, Knime, Lexalytics, Linuamatics, Mindbreeze, Sinequa, Stratifyd, Synapsify. At present, artificial intelligence can bring a lot of help to enterprises. But according to Forrester's survey last year, among companies that don't plan to invest in artificial intelligence, many companies believe that there is a barrier to the spread of artificial intelligence. 1. There is no clearly defined business scenario: 42% 2. Unclear how artificial intelligence can be used: 39% 3. Lack of necessary skills: 33% 4. Need to invest first to promote the modernization of the data management platform: 29% 5. Lack of budget: 23% 6. Uncertain what elements are required to configure an artificial intelligence system: 19% 7. Artificial intelligence system has not been proven: 14% 8. Lack of proper process or management method: 13% 9. Artificial intelligence is just a gimmick, there is no real thing: 11% 10. Do not master, or can not get the required data: 8% 11. Not sure what artificial intelligence is: 3% Forrester believes that after overcoming these barriers, companies can accelerate the transition to user-oriented applications and the development of enterprise intelligence in the Internet. KNB1-32 Miniature Circuit Breaker
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