Zeeshan Mir Baz has collected the information from this website:https://www.forbes.com/sites/gilpress/2017/01/23/top-10-hot-artificial-intelligence-ai-technologies/#34bc6cc19287 in this article

said that:

The market for artificial intelligence (AI) technologies is
flourishing. Beyond the hype and the heightened media attention, the
numerous startups and the internet giants racing to acquire them, there
is a significant increase in investment and adoption by enterprises. A
Narrative Science survey found last year that 38% of enterprises are already using AI, growing to 62% by 2018.
Forrester Research predicted a greater than 300% increase in investment in artificial intelligence in 2017 compared with 2016.
IDC estimated that the AI market will grow from $8 billion in 2016 to more than $47 billion in 2020.
Coined in 1955 to describe a new computer science sub-discipline,
“Artificial Intelligence” today includes a variety of technologies and
tools, some time-tested, others relatively new. To help make sense of
what’s hot and what’s not, Forrester just published a
TechRadar report on Artificial Intelligence
(for application development professionals), a detailed analysis of 13
technologies enterprises should consider adopting to support human
decision-making.
Based on Forrester’s analysis, here’s my list of the 10 hottest AI technologies:
- Natural Language Generation: Producing text from
computer data. Currently used in customer service, report generation,
and summarizing business intelligence insights. Sample vendors: Attivio,
Automated Insights, Cambridge Semantics, Digital Reasoning, Lucidworks,
Narrative Science, SAS, Yseop.
- Speech Recognition: Transcribe and transform human
speech into format useful for computer applications. Currently used in
interactive voice response systems and mobile applications. Sample
vendors: NICE, Nuance Communications, OpenText, Verint Systems.
- Virtual Agents: “The current darling of the
media,” says Forrester (I believe they refer to my evolving
relationships with Alexa), from simple chatbots to advanced systems that
can network with humans. Currently used in customer service and support
and as a smart home manager. Sample vendors: Amazon, Apple, Artificial
Solutions, Assist AI, Creative Virtual, Google, IBM, IPsoft, Microsoft,
Satisfi.
- Machine Learning Platforms: Providing algorithms,
APIs, development and training toolkits, data, as well as computing
power to design, train, and deploy models into applications, processes,
and other machines. Currently used in a wide range of enterprise
applications, mostly `involving prediction or classification. Sample
vendors: Amazon, Fractal Analytics, Google, H2O.ai, Microsoft, SAS,
Skytree.
- AI-optimized Hardware: Graphics processing units
(GPU) and appliances specifically designed and architected to
efficiently run AI-oriented computational jobs. Currently primarily
making a difference in deep learning applications. Sample vendors:
Alluviate, Cray, Google, IBM, Intel, Nvidia.
- Decision Management: Engines that insert rules and
logic into AI systems and used for initial setup/training and ongoing
maintenance and tuning. A mature technology, it is used in a wide
variety of enterprise applications, assisting in or performing automated
decision-making. Sample vendors: Advanced Systems Concepts,
Informatica, Maana, Pegasystems, UiPath.
- Deep Learning Platforms: A special type of machine
learning consisting of artificial neural networks with multiple
abstraction layers. Currently primarily used in pattern recognition and
classification applications supported by very large data sets. Sample
vendors: Deep Instinct, Ersatz Labs, Fluid AI, MathWorks, Peltarion,
Saffron Technology, Sentient Technologies.
- Biometrics: Enable more natural interactions
between humans and machines, including but not limited to image and
touch recognition, speech, and body language. Currently used primarily
in market research. Sample vendors: 3VR, Affectiva, Agnitio, FaceFirst,
Sensory, Synqera, Tahzoo.
- Robotic Process Automation: Using scripts and other
methods to automate human action to support efficient business
processes. Currently used where it’s too expensive or inefficient for
humans to execute a task or a process. Sample vendors: Advanced Systems
Concepts, Automation Anywhere, Blue Prism, UiPath, WorkFusion.
- Text Analytics and NLP: Natural language
processing (NLP) uses and supports text analytics by facilitating the
understanding of sentence structure and meaning, sentiment, and intent
through statistical and machine learning methods. Currently used in
fraud detection and security, a wide range of automated assistants, and
applications for mining unstructured data. Sample vendors: Basis
Technology, Coveo, Expert System, Indico, Knime, Lexalytics,
Linguamatics, Mindbreeze, Sinequa, Stratifyd, Synapsify.
- There are certainly many business benefits gained from AI
technologies today, but according to a survey Forrester conducted last
year, there are also obstacles to AI adoption as expressed by companies
with no plans of investing in AI:
There is no defined business case 42%
Not clear what AI can be used for 39%
Don’t have the required skills 33%
Need first to invest in modernizing data mgt platform 29%
Don’t have the budget 23%
Not certain what is needed for implementing an AI system 19%
AI systems are not proven 14%
Do not have the right processes or governance 13%
AI is a lot of hype with little substance 11%
Don’t own or have access to the required data 8%
Not sure what AI means 3%
Once enterprises overcome these obstacles, Forrester concludes, they
stand to gain from AI driving accelerated transformation in
customer-facing applications and developing an interconnected web of
enterprise intelligence.
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