近日，百度研究院发布了一份关于2020年人工智能科技趋势预测的报告，报告从十个角度对2020年AI的主要发展趋势做了阐述。
以下是十大预测趋势的详细解读：
趋势一：AI 技术已发展到可大规模产业化阶段，2020年将出现多家AI工厂
AI技术以及各类商业解决方案已日臻成熟，并快速进入产业化阶段。随着全球科技巨头对AI技术的持续投入，到2020年，全球范围内将出现多家人工智能模型与数据工厂，从而大规模推动人工智能技术和相关的商业解决方案更新产业。例如客服行业的AI解决方案将可以大规模复制运用到金融、电商、教育等诸多行业中。
(英文原文，下同：The increasingly mature AI technology and all types of associated business solutions are rapidly entering the stage of "industrialization". With the continuous investment global technology giants pumped into AI technology, there will be many factories of AI models and data emerging in 2020, facilitating AI technology and associated commercial solutions on a large scale to update industries. For example, AI solutions in the customer service industry can be copied and applied to finance, e-commerce, education and other industries on a large scale. )
趋势二：2020年将会是AI芯片落地的关键年
近几年，AI芯片逐渐达到了可用的状态，2020年将会是AI芯片大规模落地应用的关键一年。端侧AI芯片将更加低成本、专业化、解决方案集成化。同时，神经网络处理单元(NPU)将成为下一代端侧通用CPU芯片的基本模块，未来越来越多的端侧CPU芯片将会以深度学习为核心进行全新的芯片规划。除了芯片以外，AI还将重新定义计算机体系架构，支持人工智能训练和推理，成为异构设计架构的新思路。
(In recent years, AI chips have gradually reached a usable state, and 2020 will be a critical year for the large-scale implementation of AI chips. AI chips on the edge will be more low-cost, specialized and seamlessly integrated into downstream solutions. At the same time, the neural processing unit (NPU) will become the basic module of the next-generation edge-based general-purpose CPU chips. In the future, more and more device-based CPU chips will integrate deep learning framework as the core to their designs. In addition to chips, AI will redefine the computer architecture and support AI training and inference as a new idea of heterogeneous design architecture. )
趋势三：深度学习深入渗透产业，并大规模应用
深度学习是人工智能领域最重要，也是被产业界证明最有效的技术。以深度学习框架为核心的开源深度学习平台大大降低了人工智能技术的开发门槛，有效提高了人工智能应用的质量和效率。2020年，深度学习将大规模应用于多个行业，实施创新，加快转型升级。
(Deep learning is the most important and effective technology in the field of artificial intelligence. At the core of open-sourced deep learning platforms is the deep learning framework, which greatly lowers the development threshold of AI technology, and effectively improves the quality and efficiency of AI applications. In 2020, deep learning will be applied across many industries at scale to implement innovation and accelerate transformation and upgrading. )
趋势四：AutoML将大大降低机器学习的门槛
AutoML的快速发展将大大降低机器学习的门槛，扩大AI应用的普及率。AutoML将能够把传统机器学习中的迭代过程综合在一起，构建一个自动化的过程。研究人员只需输入元知识(如卷积运算、问题描述等)，算法就可以自动选择合适的数据、优化模型结构和配置、自动地训练模型，并将其部署到不同的设备上。
(AutoML will be able to integrate the iterative process in traditional machine learning and build an automatic process. Researchers only need to input meta-knowledge (such as convolution operations, problem descriptions, etc.), the algorithm can automatically select the appropriate data, optimize the model structure and configuration, train the model, and deploy it on different devices. The rapid development of AutoML will greatly lower the threshold of machine learning and increase the popularity of AI applications. )
趋势五: 多模态深度语义理解进一步成熟，并得到更广泛应用
多模态深度语义理解以声音、图像、文本等不同模态的信息为输入，融合感知和认知技术，实现对信息的多维度深层次理解。随着计算视觉、语音、自然语言理解和知识图谱等技术的快速发展和大规模应用，多模态深度语义理解逐渐成熟，应用场景更加广阔。结合AI芯片，将广泛应用于智能家居、金融、安防、教育、医疗等行业。
(Multimodal deep semantic understanding takes the information of different models such as voice, image, and text as input, and integrates perception and cognition technologies to achieve a multi-dimensional deep understanding of information. With the rapid development and large-scale application of computing vision, speech, natural language understanding, and knowledge graph, multimodal deep semantic understanding is gradually mature, which leads to a broader application scenario. Combined with AI chips, it will be widely used smart home, finance, security, education, healthcare, and other industries. )
趋势六：自然语言处理技术将与知识深度融合，面向通用自然语言理解的计算平台得到广泛应用
随着大规模语言模型预训练技术的出现和发展，通用自然语言理解能力有了极大地提高。基于海量文本数据的语义表示预处理技术将与领域知识深度融合，不断提高自动答疑、情感分析、阅读理解、推理、信息提取等自然语言处理任务的有效性。集合超大规模算力、丰富领域数据、预训练模型和完善研发工具的通用自然语言理解计算平台将逐渐成熟，并在互联网、医疗、法律、金融等领域得到广泛应用。
(With the emergence and development of pre-training large-scale language model, the technology of general natural language understanding has been greatly improved. Semantic representation pre-training technology based on massive text data will be deeply integrated with domain knowledge to continuously improve the effectiveness of natural language processing tasks such as automatic question answering, emotional analysis, reading comprehension, reasoning, information extraction, etc. The general natural language understanding the computing platform, which integrates large-scale computing power, rich domain data, pre-training model and improved R&D tools, will be gradually improved and widely used in the internet, healthcare, legal, financial and other fields. )趋势七：物联网将在边界、维度和场景三个领域形成突破随着5G和边缘计算的发展，算力将不再局限于云计算中心，向万物蔓延，会产生一个泛分布式计算平台。同时，对时间和空间这两个物理世界最重要维度的洞察，将成为新一代物联网平台的基本能力。这也将推动物联网与能源、电力、工业、物流、医疗、智能城市等更多场景发生融合，创造出更大的价值。(With the development of 5G and edge computing, computing power will not be limited to cloud computing centers, expanding to everything and building a distributed computing platform. At the same time, the insight into time and space, the two most important dimensions of the physical world, will become the basic capabilities of the new-generation IoT platforms. This will promote the integration of IoT with more scenarios such as energy, power, industry, logistics, medical treatment, and intelligent city, and create greater value.)
趋势八:智能交通将加速在园区、城市等多样化场景中落地
自动驾驶的发展正在趋于理性，未来几年市场对智能驾驶的发展也会更加有信心。2020年，自动驾驶汽车将被应用于物流快递、公共交通、封闭道路等不同场景。同时，V2X(vehicle to everything)技术启动规模化部署和应用，这使得车辆和道路形成一个广泛的联系，进一步推动智能车路协同技术的实现，智能交通加速在园区、城市、高速等多样化场景中落地。
(The development of autonomous vehicles is becoming more rational, and the market will be more confident in the development of intelligent driving in the next few years. In 2020, more autonomous vehicles will be applied to different scenarios such as logistics, public transport, geofenced areas and so on. At the same time, V2X (vehicle to everything) technology is ready for large-scale deployment and application, which makes vehicles and roads form a wide range of connections, further promoting the realization of Intelligent Vehicle Infrastructure Cooperative Systems (IVICS), and accelerating the implementation of intelligent transportation in parks, cities, expressways and other scenarios. )
趋势九：区块链技术将以更加务实的姿态融入更多场景
随着区块链技术与人工智能、大数据、物联网以及边缘计算的深度融合，数据与资产的线上线下映射问题将逐一解决。围绕区块链构建的数据确权、数据使用，数据流通和交换等解决方案，将在各行各业发挥巨大的作用。例如，在电商领域，可保证商品全流程数据的真实性;在供应链领域，可保证全流程数据的公开和透明，以及企业之间的安全交换;在政务领域，可以实现政府数据的打通、电子证书的实现等。
(With the in-depth integration of blockchain technology with AI, big data, IoT and edge computing, the problems concerning the online and offline mapping of data and assets will be solved one by one. Solutions such as data authorization, data use, data circulation and exchange built around blockchain will play a huge role among people from all walks of life. For example, in e-commerce, blockchain can ensure the authenticity of the whole process data of goods; in supply chain, it can ensure the openness and transparency of the whole process data, as well as the safe exchange between enterprises; in government affairs, it can achieve the opening of government data, the realization of electronic certificates and so on. )
趋势十：量子计算将迎来新一轮爆发，为AI与云计算注入新活力
随着量子霸权的成功展示，量子计算将在2020年迎来新一轮爆发。量子硬件方面，可编程的中等规模有噪量子设备的性能会得到进一步提升，并具备纠错能力。具有一定实用价值的量子算法将能够在其上运行，量子人工智能的应用将得到极大的发展。量子软件方面，高质量的量子计算平台和软件将会出现，并与AI和云计算技术深度融合。此外，随着量子计算生态产业链的初步形成，量子计算必将在更多应用领域受到更多的关注。越来越多的行业巨头陆续投入研发资源进行战略布局，这将给未来的人工智能和云计算领域带来新的面貌。
(With the successful demonstration of quantum hegemony, quantum computing will usher in a new round of explosive growth in 2020. In terms of quantum hardware, the performance of programmable medium-sized noisy quantum devices will be further improved and have the ability of error correction. Quantum algorithms with certain practical value will be able to run on them, and the application of quantum artificial intelligence will be greatly developed. In terms of quantum software, high-quality quantum computing platforms and software will emerge and be deeply integrated with AI and cloud computing technologies. In addition, with the emergence of the quantum computing industry chain, quantum computing will surely garner more attention in more application fields. More and more industry giants have invested in R&D resources for strategic layout, which has the opportunity to bring a new face to the future AI and cloud computing fields. )
