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Artificial Intelligence in Human Resource Management

The success of a company always depends on two things including a skilled workforce and efficient business strategies. Without a proper vision, you can’t achieve business targets, and of course without a dedicated workforce also a business can’t progress well in the market. Am I right?

 

 

This was the reason why most of the companies both SMEs and organizations, or MNCs are always looking to hire the right talent to meet their objectives or project goals.

In this ever-changing world, human resources (HR) have been facing various challenges in hiring the right candidate to fulfill the skill gap. Now, it is the Artificial intelligence (AI) world. Organizations are implementing AI strategies to manage their HR-related tasks more efficiently than before.

AI experts believe that proper employee engagement is the best way to retain the workforce and thus increase productivity and the revenue of the company by nearly 26%. From the number, we can understand how AI solutions for HR management are important to strengthen the whole team and able to get long-term advantages.

AI technologies in the HR industry have reached a new level and giving unbelievable benefits to the business. Artificial intelligence provides unlimited opportunities to advance HR functions like selecting, recruiting, hiring, reporting, payroll, etc.

The executives of human resources believe that the integration of Artificial Intelligence and HR administration will provide improved candidate experience.

In this blog, we have explained top 5 ways to utilize Artificial Intelligence in HR management.
Let’s Start!

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Role Of AI In Human Resource Management

The role of AI In HR Management is incredible. AI apps are assisting the HR team to handle their tasks more efficiently and improving their performance level ever like before. Here are a few significant applications of AI in Human Resources.

Significant Applications Of Artificial Intelligence (AI) In HR

AI in Human Resources has the tremendous potential to make all your HR functions automate and seamless. The use of AI in Human Resources (AI in the recruitment process) automates the recruitment workflow and saves a lot of time. Here are the possible applications of AI in HR. Let’s see how AI in HR offers miracles for HR management.

How AI Is Transforming HR Management?

How is AI Used in Human Resources?

You might have this question in your mind if you are ahead to hire the best mobile app development company USA, India, UAE for AI-powered HR management solution development.

#1. Talent Acquisition

The impact of Artificial Intelligence on HR practices will be flourishing and it is the best business case for AI in the HR industry. The role of AI in HR management for finding and shortlisting candidate profiles is one of the best possible applications of AI in HR.

HR managers can reduce or eliminate time spent on performing repetitive tasks with the help of AI technology. AI-enabled talent acquisition apps or software can read, scan, select candidate profiles, and eradicate 75% of the burden in the process of manual searching for recruitment.

Furthermore, the role of AI in HR in 2022 also allows recruiters to analyze and evaluate the performance of the candidates deeply. In such situations, HR teams can enhance the quality of the hiring decision tremendously. Hence, the organizations can save a lot of time as well as costs spent on acquiring talent.

Recommend: Top Recruiting and Hiring Trends To Know

#2 Onboarding At The Right Time

If you ask me how is Artificial Intelligence used in Human Resources, then quick onboarding at the right time is my answer.

Onboarding employees that you hired at the right time as scheduled is one of the difficult challenges for the HR people. Because there are N number of situations where the employees you hired and released offer letter will reject to join as you scheduled.

But, the implementation of AI in Human Resources makes systems more efficient and intelligent to streamline the onboard process. AI in HR is widely used for being in connection with the resources digitally and optimizes the onboarding process.

#3 Flexible Personalized Training

The impact of AI in Human Resources is far away from what we are expecting. Driven by the advancements in mobile app development technologies, organizations are beneficial to get more interactive and intelligent applications.

AI technology helps the HR team to analyze the technical capabilities of employees and helps them to update with the current skill gap in the team. AI in HR offers personalized training recommendations to employees and makes them ready to use and get expertise in the technology.

#4 Performance Analysis

With the help of AI tools, it is easy to manage the behavior and analyze the performances of each employee. HR managers utilize AI tools and set strong goals and track the performance of teams and individuals. This would be helpful for HRs for appraisals. The use of AI in Human Resources Management in this type of work results in better productivity and overall results.

#5 Employee Retention

The role of AI in HR Management has a great impact on improving employee retention. Yes, it is very tough to hire a perfect candidate, but it is tougher to keep them in a team for a long period. That’s why nearly 60% of organizations think that employee retention is one of the biggest challenges. But, Artificial intelligence can address employee issues and let them retain for the long run.

Recommend: How Artificial Intelligence Meets HR Requirements?

These are a few important and possible applications of AI in HR. Besides of above-discussed applications, AI is used in so many ways in modernizing the HR functions.

Why Human Resource Still requires People Despite These AI Strengths?

From the above discussions, we come to know that Artificial intelligence is excellent and AI tools can complete all the tasks faster and efficiently than human beings. To automate different tasks of HR, artificial intelligence is the best and valuable technology.

However, human intelligence will also be required to make final decisions to find innovative ways that keep the employees engaged in work. AI tools cannot engage employees as the HR team does. Artificial intelligence is an advanced tool that can give HR team members more time to know their staff needs, the culture of the company and solve the crucial issues.

Overall, we can say that artificial intelligence helps the HR team to make the workplace more efficient and productive.

Conclusion

Finally, AI in HR or Artificial Intelligence in HR is a boom in the market. Since the role of AI in HR Management is going to occupy a complete space in the HR management industry, AI apps development for HR management has a bright scope in the future.

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We ensure the best talent and onboard them at the right time.

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Taking inspiration from the animal kingdom, Flinders University researchers are developing affordable, flexible and highly responsive 'whiskers' to attach to robots. Their article, "Optimising electromechanical whisker design for contact localisation," has been published in the journal Sensors and Actuators A: Physical.

Industrial robot adoption is reaching new heights as manufacturers embrace Industry 4.0 initiatives. As beneficial as this movement is, it has led to some unexpected challenges for the sector. Most notably, robots are becoming a common target for cyberattacks.

Courtesy ChatGPT and Grammarly

Among ChatGPT’s myriad editorial skills is its ability to proofread your text at an extremely high level of proficiency.

And if all you’re looking for is an occasional proofread, you’ll find you can get help from ChatGPT — combined with similar AI tools — for free.

I began using ChatGPT as a back-up proofer for my primary proofreading tool — Grammarly — after I saw it consistently catching errors in text that Grammarly was missing.

Granted, the errors ChatGPT catches are generally minuscule (Grammarly really is an excellent proofreading tool, among its many other charms).

But sometimes, ChatGPT does catch outright misspellings that simply fly by Grammarly.

And on average, I find that ChatGPT spotlights about one-to-three errors that are missed by Grammarly per one thousand words of text.

One of the great beauties of creating a proofreading prompt for ChatGPT (a sample of one of mine follows) is that you can design it to do proofreading exactly as you prefer.

My proofer prompt, for example, instructs ChatGPT to retype the entire text it’s proofing, highlight each error it finds in bold, offer a suggested correction — and then explain its rationale for making each correction.

That may be overkill for some people.

But I prefer double-checking ChatGPT’s work myself and being given a sound reason for making a change — rather than simply trusting ChatGPT to ‘correct’ my text with no oversight.

I also greatly appreciate that I can add new rules to my ChatGPT proofer — or take some rules away — by simply changing a few words in the prompt.

And I really like the fact that my proofer grows more powerful with every upgrade of ChatGPT’s software.

For example: The ChatGPT proofer I use most (below) is more powerful and more meticulous in its proofreading when used with the most advanced version of ChatGPT — ChatGPT 4o — as compared to its use with previous versions of ChatGPT.

As with many things ChatGPT, your success designing a proofer hinges mightily on the precise wording of your prompt.

Sure, you can design a proofer with something simple like: “You are an expert proofreader. Please proofread the following text and highlight the errors in bold.”

But I’ve had better luck using a longer prompt that spells out — in excruciating detail — exactly what I’m looking for.

For example: In addition to asking for an error scan, I also ask ChatGPT to be meticulous as it’s scanning, to focus intensely on its task, to get extremely granular in its inspection (such as identifying correct placement of periods) and similar.

But simultaneously, I’m also careful not to overload ChatGPT with too many different kinds of requests in a single prompt — which can needlessly slow down ChatGPT, or even confuse it entirely.

As previously indicated, if you’re only looking to do some occasional proofreading, you can do so for free using ChatGPT and other tools and still enjoy a high level of performance.

For example: You can sign-up for Grammarly’s free version, which will do a basic, highly effective proofread of your text at absolutely no charge.

And then you can head over to ChatGPT, where you can double-check Grammarly’s work using free, limited access to ChatGPT’s most advanced AI engine, ChatGPT 4o.

Granted, you can only input about 15 prompts every three hours using ChatGPT-4o’s free version.

But if you only have a few documents to double-check for proofing, you’re all set.

For more extended, free proofing, you can also use Grammarly in combination with Gemini Advanced — Google’s direct competitor to ChatGPT-4o.

The reason: Gemini Advanced is based on the same genre of AI technology that powers ChatGPT. So a prompt developed for use with ChatGPT will also work flawlessly with Gemini Advanced.

Plus, Gemini Advanced currently offers an extremely generous, two-month free trial.

So chances are, you can theoretically proof an entire book if you so desired — over time — just using Grammarly’s free version, in concert with the Google Gemini Advanced’s free trial.

Another bonus: Given that the lion’s share of AI writers/tools on the market right now use the same genre of AI technology as ChatGPT and Gemini Advanced, you can also use any prompt initially developed for ChatGPT in any other AI tool based on the same genre of AI tech, including:

Jasper

Sudowrite

Anyword

INK

Scalenut

Neuraltext

Writesonic

Wordtune

Sapling

Notion Labs

Copy.ai

Rytr

Chibi AI

Surfer

Article Forge

WordAI

AI Writer

Hypotenuse AI

Longshot

CreaitorAI

CopySmith

OpenAI

Writer

GrowthBar

Closerscopy

ParagraphAI

Frase

A final tip before we take a look at a sample proofer: It’s always best to design your proofer as a stand-alone prompt — followed by a second prompt that you use to actually input the text you want proofed.

This technique ensures that you’re not giving ChatGPT too many words and/or too much data to process in any one prompt.

Even better: Inputting your text in a second prompt also expands the number of words ChatGPT will proofread for you, given that ChatGPT can only process and understand so many words in any given prompt.

In any event, here’s one of the homespun proofers I use when I’m proofing with Grammarly and I’m looking for ChatGPT to do an extremely granular double-check of Grammarly’s work:

Joe Dysart’s Proofer Prompt:

[You are a seasoned, award-winning expert in proofreading. Please conduct a meticulous, line-by-line proofread of text that I will provide you in the next prompt.

Focus intensely on the following aspects:

*Technical Accuracy: Identify and correct errors in spelling, grammar, punctuation, capitalization and formatting.

*Other Technical Errors: Identify and correct other technical errors not specifically listed in this prompt.

*Correct Placement of Periods and Quotes: Pay special attention to the placement of periods and quotes. Identify and correct any incorrect placement of periods and quotes and incorrect spacing associated with them.

*Consistency: Identify and correct inconsistent use of terminology, names, spelling, formatting and internal style rules (if applicable).

*Specific Conventions: Verify that the following conventions are employed:

~Allow the use of slang
~Allow the use of sentence fragments
~Allow the use of sentences that start with And
~Allow the use of sentences that start with But

To report the errors you find:

*Preserve Original: Retype the entire text with your changes.

*Highlight Errors: Highlight each error you find in bold.

*Provide Correction: Offer your correction for each error in parentheses directly after the bolded error.

*Explain Correction: For each correction you make using boldface type, please explain the correction, also in boldface type, using the format in the following example, which is delineated by three quote symbols “””solider (soldier) — The word soldier was misspelled.”””

*Always keep in mind your overarching goal: Your overarching goal is to provide the most comprehensive, meticulous proofreading of the text as possible.

Please indicate that you fully understand these proofreading instructions and have stored the instructions with the statement: I fully understand these proofreading instructions and have stored the instructions. Please input the text you’d like proofread with your next prompt using the words, “Here’s the text to proofread:”]

Share a Link:  Please consider sharing a link to https://RobotWritersAI.com from your blog, social media post, publication or emails. More links leading to RobotWritersAI.com helps everyone interested in AI-generated writing.

–Joe Dysart is editor of RobotWritersAI.com and a tech journalist with 20+ years experience. His work has appeared in 150+ publications, including The New York Times and the Financial Times of London.

The post Free AI-Powered Proofreading appeared first on Robot Writers AI.

A combined team of roboticists from Stanford University and the Toyota Research Institute has found that adding audio data to visual data when training robots helps to improve their learning skills. The team has posted their research on the arXiv preprint server.

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Researchers tested phononic nanomaterials designed with an automated genetic algorithm that responded to light pulses with controlled vibrations. This work may help in the development of next-generation sensors and computer devices.

Without any external input to guide its actions or decisions, a robot would behave like a machine, performing tasks without adaptation or autonomy. Serving as the critical link between the robot and its environment, sensors enable the robot to perceive and interpret.

“In the new future, every single interaction with the digital world will be through an AI assistant of some kind. We will be talking to these AI assistants all the time. Our entire digital diet will be mediated by AI systems,” Meta’s Chief AI Scientist Yann LeCun said at a recent Meta event. This bold prediction underscores a transformative shift in how we engage with technology, hinting at a future where AI personal assistants become indispensable in our daily lives.

LeCun’s vision is echoed across the tech industry. Demis Hassabis, CEO of Google DeepMind, emphasized their commitment to developing a universal agent for everyday life. He pointed out that this vision is the driving force behind Gemini, an AI designed to be multimodal from inception, capable of handling a diverse range of tasks and interactions.

These perspectives illustrate a consensus among leading AI researchers and developers: we are on the cusp of an era where AI personal assistants will significantly enhance both our personal and professional lives. Comparable to Tony Stark’s JARVIS, these AI systems are envisioned to seamlessly integrate into our routines, offering assistance and enhancing productivity in ways that were once the realm of science fiction.

However, to gauge our progress towards this ambitious goal, it is essential to first delineate what we expect from an AI personal assistant. Understanding these expectations provides a benchmark for evaluating current advancements and identifying areas that require further innovation.

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What We Expect from AI Personal Assistants

While certain features of an AI personal assistant might carry more weight than others, the following aspects form the foundation of an effective and useful assistant:

Intelligence and Accuracy. An AI personal assistant must be capable of delivering precise and reliable information, drawing from high-quality, credible sources. The assistant’s ability to comprehend and accurately respond to complex queries is essential for its effectiveness.

Transparency and Reliability. One critical expectation is the AI’s ability to acknowledge its limitations. When it lacks the information or is uncertain about an answer, it must clearly communicate this to the user, instead of ‘hallucinating.’ Otherwise, it doesn’t make much sense to have an assistant whose responses you always need to verify.

Multimodal Functionality. A robust AI personal assistant should be multimodal, capable of processing and understanding text, code, images, videos, and audio. This versatility ensures it can handle a wide range of tasks and inputs, making it highly adaptable and useful in various contexts.

Voice Accessibility. An AI assistant should be easily accessible via voice commands. It should respond quickly and naturally, mirroring the pace and quality of human communication. This instant accessibility enhances convenience and efficiency.

Real-time Streaming. The assistant should be always-on, omnipresent, and available across multiple channels. Whether through smartphones, smart speakers, or other connected devices, the AI must provide real-time assistance whenever and wherever needed.

Self-learning Abilities. You want your assistant to know your specific routines and preferences, but it is impractical to define exhaustive rules for every potential interaction. Therefore, an AI personal assistant should possess self-learning capabilities, allowing it to adapt and improve through interactions with a specific user. This personalized learning helps the assistant become increasingly effective over time

Autonomous Actions. Beyond providing information, a valuable AI assistant should have the autonomy to take action when necessary. This could include various tasks like managing calendars, making reservations, or sending emails, thereby streamlining tasks and reducing the user’s workload.

Security and Privacy. In an era where data security is paramount, AI personal assistants must ensure robust security measures. Users need confidence that their interactions and data are protected, maintaining their privacy and safeguarding against potential breaches.

Progress and Current Innovations

So where are we now? We obviously don’t yet have AI personal assistants that meet all the above criteria. But there are some tools that introduced significant breakthroughs in this area. Not surprisingly, they come from leading AI tech companies.

OpenAI’s GPT-4o

This May, OpenAI introduced their new flagship model, GPT-4o (“o” for “omni”). It marks a significant step towards more natural human-computer interaction. The model accepts input in any combination of text, audio, image, and video, and it can generate outputs in text, audio, and image formats. This multimodal capability positions GPT-4o as a versatile assistant for a variety of tasks.

Crucially, GPT-4o can be easily accessed via voice commands, supporting natural conversations with an impressive response time averaging 320 milliseconds, comparable to human interaction speeds. This accessibility and speed make it a strong candidate for real-time assistance in everyday scenarios.

In terms of intelligence, GPT-4o matches or exceeds the performance of GPT-4 Turbo, which currently leads many benchmarks. However, like other large language models, it remains prone to mistakes and hallucinations, limiting its use in tasks where accuracy is paramount. Despite these limitations, GPT-4o includes self-learning features, allowing it to improve responses based on user feedback. This partial self-learning ability helps it adapt to user preferences over time, though it is not yet as advanced as the personalized assistance envisioned in a JARVIS-like system.

While GPT-4o offers enhanced interaction capabilities, it does not perform autonomous tasks. Moreover, privacy remains a significant concern, as with many AI-powered tools, underscoring the need for robust security measures to protect user data.

Finally, OpenAI has not yet released GPT-4o with all the multimodal capabilities showcased in their demo videos. Currently, the public can only access the model with text and image inputs, and text outputs. Real-world testing of the model may uncover additional weaknesses.

Google’s Astra

Announced just a day after OpenAI’s GPT-4o, Google DeepMind’s Astra represents another significant leap in AI personal assistant technology. Astra responds to audio and video inputs in real time, much like GPT-4o, promising seamless interaction and immediate assistance.

The demo showcased Astra’s impressive capabilities: it could explain the functionality of a piece of code simply by observing someone’s screen through a smartphone camera, recognize a neighborhood by viewing the scenery from a window, and even “remember” the location of an object shown earlier in the video stream. Notably, part of the demo featured a user employing smart glasses instead of a phone, highlighting the potential for more integrated and innovative user experiences.

However, this remains an announcement, and the public does not yet have access to Astra. Thus, its real-world capabilities are still to be tested. It is likely that Astra, like other AI models, will still be prone to hallucinations and does not yet perform autonomous tasks. Nevertheless, the Google DeepMind team behind Astra has expressed a vision of developing a universal agent useful in everyday life, which suggests future iterations may include autonomous task performance.

Other Promising Players

As the race to develop advanced AI personal assistants heats up, several other major tech companies are making strategic moves, hinting at their imminent entries into this competitive arena. Although their next-generation AI personal assistants are yet to be launched, recent developments indicate significant progress.

Microsoft

Earlier this year, Microsoft acqui-hired Inflection, the company focused on developing “Pi, your personal AI.” While technically not an acquisition, Microsoft hired key staff members, including Mustafa Suleyman and Karen Simonyan, and paid approximately $650 million, mostly in the form of a licensing deal that makes Inflection’s models available for sale on the software giant’s Azure cloud service. Considering Mustafa Suleyman’s strong belief in personal artificial intelligence, this might be an indication that Microsoft is likely to offer its own personal AI assistant in the near future.

Amazon

Amazon, a pioneer in the voice assistant market with Alexa, remains committed to its mission of making Alexa “the world’s best personal assistant.” Recently, Amazon executed a strategy similar to Microsoft’s by hiring the co-founders and key employees of Adept AI, a startup known for developing AI-powered agents. The technology developed by Adept AI was licensed to Amazon, with the team joining Amazon’s AGI division to build real-world digital agents. Whether Amazon’s new product will cater primarily to enterprise customers or also introduce a personal AI assistant remains to be seen. However, integrating this technology could finally transform Alexa into a more powerful, conversational LLM-powered assistant. Currently, the old Alexa is hindering progress as Amazon has not yet figured out how to integrate the existing Alexa capabilities with the more advanced, conversational features touted for the new Alexa last fall.

Apple

Another leader in voice assistants, Apple, is also busy improving Siri. The company is partnering with OpenAI to power some of its AI features with ChatGPT technology, while also building its own models. Apple’s published research indicates a focus on small and efficient models, aiming to have all AI features running on-device, fully offline. Apple is also working on making the new AI-powered Siri more conversational and versatile, allowing users to control their apps with voice commands. For example, users will be able to ask the voice assistant to find information inside a particular email or even surface a photo of a specific friend. Apple places a strong emphasis on security, with the system automatically deciding whether to use on-device processing or contact Apple’s private cloud computing server to fulfill requests.

These strategic moves by Microsoft, Amazon, and Apple reflect a broader trend towards more sophisticated, user-friendly AI personal assistants. As these companies continue to innovate and develop their technologies, we can anticipate significant advancements in the capabilities and functionalities of AI personal assistants in the near future.

The Road Ahead

The race to develop the next generation of AI personal assistants is intensifying, with major tech companies like OpenAI, Google, Microsoft, Amazon, and Apple making significant strides. Each of these players brings unique innovations and perspectives, pushing the boundaries of what AI can achieve in our daily lives. While we are not yet at the point where AI personal assistants meet all the ideal criteria, the advancements we see today are promising steps toward a future where these digital companions become an integral part of our personal and professional lives. As the technology continues to evolve, the vision of having a truly intelligent, multimodal, and autonomous AI assistant appears closer than ever.

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The post Next-Gen AI Assistants: Innovations from OpenAI, Google, and Beyond appeared first on TOPBOTS.

Medical technology relies on FAULHABER not only in robot-supported procedures. For example, drive systems are used in implantable heart pumps, surgical hand tools, in ophthalmology, in cosmetics and in medical imaging and image processing.

With a new surgical intervention and neuroprosthetic interface, researchers restored a natural walking gait in people with amputations below the knee. Seven patients were able to walk faster, avoid obstacles, and climb stairs more naturally than people with a traditional amputation.

Computer scientists have invented a camera mechanism that improves how robots see and react to the world around them. Inspired by how the human eye works, their innovative camera system mimics the tiny involuntary movements used by the eye to maintain clear and stable vision over time.

As an undergraduate engineering student in Delhi, India, Amisha Bhaskar took a field trip to a facility for disabled war veterans and met a man who had lost both hands. When she asked him what technologies could improve his life, his reply left an indelible impression: He wanted something so he could take care of himself and not be forced to rely upon others.

A team led by University of Maryland computer scientists has invented a camera mechanism that improves how robots see and react to the world around them. Inspired by how the human eye works, their innovative camera system mimics the tiny involuntary movements used by the eye to maintain clear and stable vision over time.

Researchers have developed nanorobots that kill cancer cells in mice. The robot's weapon is hidden in a nanostructure and is exposed only in the tumour microenvironment, sparing healthy cells.