Could anyone suggest me topics in AI?

Hi, Good to know that you are taking up a next-gen OB related topic.

I can see prospects of AI in Industrial psychology in the following areas

1) sentimental analysis..

2) job-fit analysis.

3) attrition retention prediction

however, in recruitment and selection, data mining tools can yield the best candidate benchmark profile. Organizations can select the appropriate candidates against that benchmark profile from different job portals and other available resource pool to filter out the best candidates to be called for interviews. You can also look for BPM, BI , Cloud computing , Analytics etc prospects to get a clearer picture of the application of AI in HRM areas.

Santosh J. Dubey

Rather, I suggest you focus on employee fitness assessment through psych profiles... it's probably best suited for AI-related work in Business Psychology.

Hope this helps.

Shafagat Mahmudova

Dear Gayathiri Sridharan ,

Look the link, maybe useful.

https://www.crazyengineers.com/threads/artificial-intelligence-project-ideas-topics-for-engineering-students.58508/

https://www.researchgate.net/post/Can_anyone_suggest_me_some_phd_research_topic_from_machine_learning_and_deep_learning

Regards, Shafagat

Borden Mushonga

1. How relevant is AI for different systems such as a) Pastoral production systems b) small holder production systems c) subsistence rural production systems

Mahfuz Judeh

Innovation is a fundamental feature of human intelligence, and a challenge for Artificial intelligence. This could be a good topic.

Iping Supriana

Hello Gayathriri, this my advice input:

The basic principle in utilizing AI is if we have a fact that has a RELATIONSHIP.

In my opinion, there are 3 levels of interesting research on AI: applied level, technology level and fundamental level.

At the applied level the important research question is how the RELATIONSHIP will be interpreted in an innovative way.

At the technological level, an important research question is how to use technology (software and hardware) to build a high-performance service based on the above RELATIONSHIP MODEL.

At the fundamental level of an important research question is how do we build INFERENCE ENGINE models to reduce the level of complexity that can occur.

Khalid Jawad

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. Visiting this source is a must.

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.”

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