5 AI Trends We Can Expect to See in 2017 and Beyond

简介: Artificial Intelligence (AI) continues to make big strides as a changemaker across various industries, unlocking significant opportunities to transf

111595541c37290554b743502a56da4c067bf175

Artificial Intelligence (AI) continues to make big strides as a changemaker across various industries, unlocking significant opportunities to transform the world as we know it.. It is expected that the AI market will be worth USD $16.06 billion by 2022, growing at a compound annual growth rate (CAGR) of 62.9% from 2016, according to research firm Matkets and Makets. AI has made notable progress in recent years and now companies are exploring its practical applications to drive business growth.  Here, we will examine emerging AI trends and how they’re changing our world. .

AI will permeate every industry

With the widespread adoption of AI across a broad range of industries, the trend of cognitive computing will continue in 2017, bringing us several advantages via data processing and capacity. AI has already made several breakthroughs in the healthcare industry. In fact, IDC forecasted a CAGR of 69.3% for investment in healthcare AI over a five-year period, especially in the areas of pharmaceutical research, diagnosis and treatment systems.

Other sectors that AI is changing significantly are banking and e-commerce, which are now using the technology for real-time monitoring, market analysis, customer relationship management and risk control. The advanced algorithms and techniques AI brings to the table has added real business value as a result.

Machine will emerge as the dominant technology 

Machine learning’s predictive capacity has supported several products to help businesses improve their processes and operations. Key players in global markets such as IBM, Alibaba Cloud, Google and Microsoft have made notable developments in advancing their own machine learning technologies and algorithms. Its applications include image and video recognition, speech analysis, language processing, search engines, and more.

Going beyond the visual

Chatbots are growing increasingly popular with messaging applications, particularly for marketers and retailers. All kinds of chatbots were introduced to the market in 2016, including over 11,000 bots that went live on Facebook Messenger. Other companies including Microsoft Tay and Google AlphaGo have been joining the trend.

Making use of natural user interfaces including voice, visual and chat, these software programs can process natural language and communicate with consumers through messaging services or email. The power of voice is emerging and this is shaping a new definition of how brands make use of these new touchpoints to communicate with consumers.

Intelligent applications on the rise

Enterprises across industries have been incorporating AI technology into their existing IT systems, in particular web and mobile apps. AI supports technology such as digital assistants with prioritizing abilities to facilitate operations, scheduling meetings and providing advanced analytics.

According to Gartner, it is expected that most of the world’s 200 largest companies will employ intelligent apps and utilize the full suite of big data and analytics tools by 2018.

AI Hardware

Hardware will still play an important role in supporting AI technology. As the leading chipmaker in the world, Intel just unveiled its robust AI platform – Intel® Nervana™ during its Intel AI Day event in November, testing AI-specific hardware optimized for neural networks to deliver the highest performance.

Other developers such as AMD are racing to develop hardware specifically for AI applications, such as robots, self-driving cars and drones. IDC forecasted that the revenue of AI hardware will increase at a CAGR of more than 60% over the next five years. Engineers and developers will continue to innovate on both the hardware and software fronts, transforming the way businesses operate and how they leverage data.

With the fascinating developments being made in the AI sphere, there is still a challenge for companies struggling to keep pace as this sophisticated technology continues to evolve. In order to reap the best benefits, these companies have to start thinking about what they need from a fundamental perspective and therefore reach out to the right vendors who can provide the right technological solutions to their needs.

目录
相关文章
|
运维 数据中心 监控
We Make AI-Ops Happen!
在云计算、大数据、人工智能、物联网、区块链技术的发展日新月异的今天,2018杭州·云栖大会于9月19-22日在杭州云栖小镇举办,本届云栖大会的会议主题为“驱动数字中国”,在170多场的前沿峰会和分论坛中,9月19下午200人的“智能运维”专场各位引人注目,现场爆满。
2521 0
|
运维 监控 云栖大会
We Make AI-Ops Happen 杭州云栖大会——智能运维专场即将开启
支持百万级规模服务器管控,保障双十一世界级工程生产运行安全的智能运维体系;直击阿里全球运行指挥中心双11的隐形战场;大规模文件分发系统,承载了整个阿里集团文件分发。We Make AI-Ops Happen 杭州云栖大会——智能运维专场即将开启!
3079 0
|
人工智能
How AI can fight the phenomenon of fake news
One of the more unsettling developments in recent months has been the phenomenon of 'fake news', where audiences consume and share news stories on social media which are not factually correct.
1866 0
|
人工智能
The 4 ethical issues in AI we're all thinking about
Everyone's keeping an eye on artificial intelligence. This technology has advanced at such a rapid rate that its impact on people's lives so far has been mind-blowing.
2409 0
|
10天前
|
机器学习/深度学习 人工智能 自然语言处理
AI技术深度解析:从基础到应用的全面介绍
人工智能(AI)技术的迅猛发展,正在深刻改变着我们的生活和工作方式。从自然语言处理(NLP)到机器学习,从神经网络到大型语言模型(LLM),AI技术的每一次进步都带来了前所未有的机遇和挑战。本文将从背景、历史、业务场景、Python代码示例、流程图以及如何上手等多个方面,对AI技术中的关键组件进行深度解析,为读者呈现一个全面而深入的AI技术世界。
67 10
|
3天前
|
机器学习/深度学习 人工智能 物联网
AI赋能大学计划·大模型技术与应用实战学生训练营——湖南大学站圆满结营
12月14日,由中国软件行业校园招聘与实习公共服务平台携手魔搭社区共同举办的AI赋能大学计划·大模型技术与产业趋势高校行AIGC项目实战营·湖南大学站圆满结营。
AI赋能大学计划·大模型技术与应用实战学生训练营——湖南大学站圆满结营
|
15天前
|
机器学习/深度学习 人工智能 自然语言处理
转载:【AI系统】AI的领域、场景与行业应用
本文概述了AI的历史、现状及发展趋势,探讨了AI在计算机视觉、自然语言处理、语音识别等领域的应用,以及在金融、医疗、教育、互联网等行业中的实践案例。随着技术进步,AI模型正从单一走向多样化,从小规模到大规模分布式训练,企业级AI系统设计面临更多挑战,同时也带来了新的研究与工程实践机遇。文中强调了AI基础设施的重要性,并鼓励读者深入了解AI系统的设计原则与研究方法,共同推动AI技术的发展。
转载:【AI系统】AI的领域、场景与行业应用
|
10天前
|
机器学习/深度学习 人工智能 算法
探索AI在医疗诊断中的应用与挑战
【10月更文挑战第21天】 本文深入探讨了人工智能(AI)技术在医疗诊断领域的应用现状与面临的挑战,旨在为读者提供一个全面的视角,了解AI如何改变传统医疗模式,以及这一变革过程中所伴随的技术、伦理和法律问题。通过分析AI技术的优势和局限性,本文旨在促进对AI在医疗领域应用的更深层次理解和讨论。
|
15天前
|
人工智能 缓存 异构计算
云原生AI加速生成式人工智能应用的部署构建
本文探讨了云原生技术背景下,尤其是Kubernetes和容器技术的发展,对模型推理服务带来的挑战与优化策略。文中详细介绍了Knative的弹性扩展机制,包括HPA和CronHPA,以及针对传统弹性扩展“滞后”问题提出的AHPA(高级弹性预测)。此外,文章重点介绍了Fluid项目,它通过分布式缓存优化了模型加载的I/O操作,显著缩短了推理服务的冷启动时间,特别是在处理大规模并发请求时表现出色。通过实际案例,展示了Fluid在vLLM和Qwen模型推理中的应用效果,证明了其在提高模型推理效率和响应速度方面的优势。
云原生AI加速生成式人工智能应用的部署构建
|
20天前
|
机器学习/深度学习 人工智能 JSON
【实战干货】AI大模型工程应用于车联网场景的实战总结
本文介绍了图像生成技术在AIGC领域的发展历程、关键技术和当前趋势,以及这些技术如何应用于新能源汽车行业的车联网服务中。
309 34

热门文章

最新文章