As global organizations keep vying for top talent, the competition for the best available talent is at its peak. When one talent resigns only after a few months of hiring, it might depict the onset of some HR crisis. This raises the question, ‘How can AI help with employee engagement?’ With rapid predictive analytics and assessment, AI aims at revolutionizing the employee engagement scenario to its fullest. Let us know how!

AI and Employee Engagement

Different organizations will have different specifications for employee engagement.  However, most organizations tend to limit employee interaction to annual performance assessments and reviews. It is quite difficult to obtain deeper insights into relevant engagement issues out of such limited interactions. To top it all, traditional conservative approaches and motivational strategies to boost employee engagement are no longer viable in the modern competitive landscape. 

Therefore, the advent of AI is an inviting change in the manner in which organizations reach out to employee engagement. AI allows organizations to modernize their respective processes -right from onboarding to performance evaluation, talent development, and so more. 

Predictive Analytics and Behavioral Mapping with AI

L&D or Learning & Development is a major driving force in employee engagement. A number of global organizations emphasize the importance of L&D. It is regarded as a crucial aspect in developing and retaining talent with the help of professional career growth and development. 

With the help of AI-powered predictive analytics or data analytics, organizations can easily come up with customized training programs. These programs can then be aligned with different learning trends and patterns of the employees. Therefore, it helps in achieving improved synergy between employee engagement and training programs. AI can help employees in real-time through the creation of adaptable training courses for helping them effectively engage at work. Some organizations also make use of the AI technology to use it ‘as a service’ towards delivering training as dedicated instructors for training programs that are computer-driven. 

AI helps HR to work over the quitting tendencies of the employees by delivering predictive analytics and behavioral mapping characteristics. With the help of AI, HR managers can look forward to developing highly accurate and personalized responses for avoiding circumstances related to employee engagement issues. Moreover, the employees who feel valued by the organization usually stay motivated and help in the overall growth of the business.

Top Benefits of AI in Predictive Assessment for Organizations

As businesses continue exploring emerging technologies like AI and ML, here are some benefits to expect:

#Improved Customer Experience and Overall Efficiency

One of the major challenges of IT organizations is increasing efficiency and boosting customer experiences. AI and predictive assessment have the capability to implement both by helping teams work efficiently. These revolutionary solutions also help in maintaining services and systems for enabling the perfect customer experience.

#Predictive Maintenance

Some systems and services -especially the ones involved in manufacturing and other types of process-intensive industries, will require ad-hoc and routine maintenance. Regular maintenance in such domains is required for achieving optimal efficiency and top-class performance.

AI and predictive assessment can leverage data obtained from a wide range of sources to signal teams when respective machines, services, systems, or infrastructure will require maintenance. It takes place before any outage or degradation can occur.

#Efficiency Improvements for Ensuring Digital Transformation

In addition to the overall improvements in efficiency, AI and predictive assessment can also be helpful in ensuring the digital transformation of the entire organization. AI and predictive analytics technologies can be embedded into the working of a particular team -its people, the tools, processes involved, and so more. 

The respective technologies are helpful in empowering teams to totally transform the manner in which the subsequent factors will come together for working in new and efficient ways.

#Trending Consumer Behaviors

Human capital will always have the best perspective on the entire business. However, Machine Learning and Artificial Intelligence can look out for the ongoing trends that a human mind might not. With the right use of Machine Learning and AI capabilities, for instance, a business can think of understanding underlying patterns in the journey of the customer. 

Organizations can then leverage the same to optimize the overall experience while driving acquisition and retention. It ultimately results in improved revenues for the entire business while improving customer satisfaction.

#Holistic 360-degree Views Across IT and Business

In addition to applications of AI, predictive assessment and Machine Learning are emerging technologies that are often available with a platform. These technologies are able to ingest a wide range of data from multiple sources. 

An important value in exploring Artificial Intelligence is that when you do so, it can help the entire organization work more efficiently with data across diverse systems. It also helps in uniting data and creating the most powerful applications of AI.

AI Helping with Organizational Research and Data Analytics

Machine Learning and AI technologies can be used for analyzing data quite efficiently. It can be helpful in the creation of advanced predictive algorithms and models for processing data and understanding the potential of outcomes across different scenarios and trends.

Moreover, the revolutionary computing capabilities of Artificial Intelligence can also result in speeding up the analysis and processing of data for further research & development. 

Conclusion

There are several more benefits of Artificial Intelligence that traverse across the entire organization. Technology continues evolving steadily. Moreover, it has more potential to be more intelligent than ever before. AI and predictive assessment will continue benefitting businesses on a daily basis. 

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Still, I can not believe what he says Capgemini, has asked more than 12,000 consumers using wizards virtual voice or text / chat regarding products and services companies in the automotive, consumer products and retail, and banking and insurance in 10 countries -United States, Sweden, Italy, India, Netherlands, France, Spain, United Kingdom, Germany and Norway-, and a thousand executives of organizations operating in the aforementioned sectors, including companies that work exclusively by Internet.

The study shows that consumers increasingly prefer to interact with Voice assistants than with human beings, especially when it comes to inquiring about products, obtaining information about new services or tracking their orders. According to the report, about 70% of consumers surveyed say that, over the next three years, they will gradually replace visits to physical stores, dealerships or the bank with smart speakers.

On the other hand, companies, Capgemini says, are already seeing the benefits of conversational assistants and place them as a fundamental element for loyalty and, in general, to enrich the customer experience. More than three – quarters of companies (76%) report having achieved quantifiable benefits of initiatives implementation of voice assistants or text/chat in their processes, and 58% say that those benefits have covered and even exceeded their expectations. Among these advantages is the reduction in more than 20% of the costs of customer service.

However, the report shows that the pace of development and effective implementation of virtual assistants by companies is lagging behind the intensity of consumer enthusiasm and demand. According to the study, less than 50% of the world’s top 100 organizations in the automotive, consumer and retail and banking and insurance sectors have voice assistants; and the same can be said for text assistants/chats.

The report also shows that consumers value the increasing capacity of conversational assistants to improve their experience. In 2017, 61% expressed their satisfaction with the use of the virtual voice assistant, such as Google Assistant, Amazon Alexa or Siri, on their smartphones; a proportion that rises to 72% in the 2019 study. In addition, in 2017 46% of consumers declared themselves satisfied with the use of a smart speaker , such as Google Home or Amazon Echo, and 44%, with an assistant Voice with smart screen, such as Amazon Echo Show and Amazon Fire TV; figures that grow to 64% and 57% in 2019, respectively.

“Once the trust with virtual assistants has been carved out, consumers are willing to move to a new level in their relationship of use, with higher levels of personalization, emotional connection and value,” says the consultant’s report. More than two thirds (68%) of consumers value that a voice assistant allows performing several tasks simultaneously in hands-free mode and 59%, that text/chat assistants are continuously improving the level of customization. The study also shows that users aspire to a more humane interaction with their assistants: 58% would like to be able to personalize voice assistants, for example, by giving them a name (55%) or defining their personality (53%).

The report also identifies four critical factors that companies must consider taking advantage of this growing consumer interest in conversational interfaces: finding the right balance between human and robotic interactions to establish stronger and lasting relationships with the customer; provide conversational assistants with additional features, such as images and videos; dedicate more efforts to gain consumer confidence by solving friction points, offering individualized information of greater interest to each user, and designing and selecting use cases that demonstrate the positive impact on the experience compared to other business criteria ; and develop skills in user experience design, architecture / technology and regulatory compliance.

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The result? Companies are increasing the pace of investment in AI systems as a defence against the next generation of cyberattacks. Two thirds (69%) of companies recognize that they will not be able to respond to critical threats without artificial intelligence. With an increasing number of devices for end-users, networks and user interfaces as a result of advances in the cloud, the internet of things (IoT), 5G and conversational interface technologies, organizations face the urgent need to continuously expand and improve your cybersecurity system by using the best firewall .

More attacks, and much faster

“AI-based cybersecurity is essential today,” says the Capgemini study, and more than half (56%) of managers say their cybersecurity analysts are overwhelmed by the immense volume of information units they must monitor to Detect and prevent intrusions. In addition, the type of cyberattacks that require immediate intervention or that cannot be solved quickly enough by cyber-analysts has grown considerably.

Specifically, cyber-attacks affecting applications subject to temporary requirements are detailed. 42% answer that the incidents have increased. On average, the type of these incidents has grown by 16%; and automated attacks, at speeds that only one machine can reach, that mutate at a rate that cannot be neutralized by traditional response systems (43% declare an increase in attacks with this speed, and in turn, these offensives have grown a 15% on average

Given these new threats, a clear majority of companies (69%) believe that they will not be able to respond to cyberattacks without the use of AI, and 61% say they need artificial intelligence to identify critical threats. One in five executives has experienced a cybersecurity gap in 2018, of which 20% cost their organization more than $ 50 million.

Consequently, about half of the managers asked by Capgemini (48%) answer that the budgets for AI in cybersecurity will increase in 2020 by almost a third (29%). In terms of deployment, 73% are conducting use case trials for artificial intelligence in cybersecurity. Only one in five companies used AI in the cybersecurity area before 2019, but a meteoric expansion of its adoption is expected: about two in three (63%) organizations plan to deploy artificial intelligence in 2020 to reinforce their systems of defending.

However, there are important barriers to the implementation of AI on a general scale. The most important challenge for the implementation of artificial intelligence in the field of cybersecurity is the difficulty in transforming the cases of use of proof of concept into a complete deployment. at all levels of the organization. 69% of respondents admit that they are having difficulties in this area.

On the other hand, half of the organizations consulted also point out the problem of integration with their infrastructures, data systems and context of current applications. While most managers replied that knows what he wants to achieve with cybersecurity based on IA, only half (54%) identified data sets required for operating the algorithms of artificial intelligence.

Also read: Cyber ​​Attacks Generate An Impact Of 40,000 Million Euros In A Year

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But today’s AI systems are far from perfect. They tend to reflect the prejudices of the data used to train them and to break down when faced with unexpected situations . They can be fooled, as we have seen in the case of controversies surrounding false information on social networks, violent content posted on YouTube , or Tay’s famous case , the Microsoft chatbot , which was manipulated to make sexist statements within hours.

Do we really want to transform these fragile bias-prone technologies into the cornerstone of tomorrow’s economy?

Minimize risk

One way to minimize the risks of AI is to increase the diversity of the teams involved in its development. As research on collective decision making and creativity indicates , groups that are cognitively more diverse tend to make better decisions. Unfortunately, this is not by far the case of the community that is currently developing AI systems. And the lack of gender diversity is an important dimension (although not the only one) of this.

An analysis published this year by the AI ​​Now Institute revealed that less than 20% of researchers requesting to participate in prestigious AI conferences are women, and that these represent only a quarter of AI university students at Stanford and at the University from California in Berkeley.

The authors claimed that this lack of gender diversity results in AI failures that only affect women, such as an Amazon hiring system that discriminated against job seekers with female names.

Our recent Gender Diversity report on AI research includes a big data analysis of 1.5 million arXiv works, a prepublication website that the AI ​​community uses very often to disseminate its work.

We analyze the text of summaries to determine which apply AI techniques, we deduce the authors’ gender from their names and study the levels of gender diversity in AI and its evolution over time. We also compare the situation in various research fields and countries, and the differences in language between works with female co-authors and works with only male authors.

Our analysis confirms the idea that there is a crisis of gender diversity in AI research. Only 13.8% of the authors of AI in arXiv are women and, in relative terms, the proportion of AI works of which at least one woman has co-authored has not improved since the 1990s.

There are significant differences between countries and research fields. We found a greater female representation in AI research in the Netherlands, Norway and Denmark, and a smaller representation in Japan and Singapore. We also found that women who work in physics, education, biology and social aspects of computer science are more likely to publish papers on AI versus those who work in computer science or mathematics.

In addition to measuring the gender diversity of AI research staff, we also explore the semantic differences between research papers with and without female participation. We test the hypothesis that research teams with more gender diversity tend to increase the variety of issues and issues that are considered in AI research, which makes their results potentially more inclusive.

To do this, we measure the “semantic signature” of each work using a machine learning technique called word embeddings ( word mapping) and compare the signatures of the works in which at least one author was a woman with those of jobs without No author.

This analysis, which focuses on the Machine Learning and Social Aspects of Computer Science in the United Kingdom, showed significant differences between the groups. Specifically, we found that work with at least one co-author tends to be more practical and socially sensitized, and in them terms such as “justice,” “human mobility,” “mental,” “gender,” and “personality” play a key role. . The difference between the two groups is consistent with the idea that cognitive diversity has an impact on the research produced and indicates that it results in a greater commitment to social issues.

How to fix it

So how do you explain this persistent gender gap in AI and what can we do about it?

Research shows that the lack of gender diversity among science, technology, engineering and math (STEM) workers is not the product of a single factor: stereotypes and gender discrimination, the lack of models and mentors, insufficient attention to the balance between work and private life and the “toxic” work environments of the technology industry come together to create a perfect storm against gender inclusion.

Ending the gender gap in AI research has no easy solution. Changes throughout the system to create safe and inclusive spaces that support and encourage researchers from groups with little representation, a change in attitudes and cultures in the fields of research and industry and better communication of the transformative potential of the AI in numerous areas could be part of it.

Political interventions, such as 13.5 million pound state investment to foster the diversity of roles in AI through new university transformation courses, may improve the situation a bit, but large-scale interventions are needed to create better connections between arts, humanities and AI, and change the image of who can work in AI.

Although there is no single reason for girls to disproportionately stop enrolling in subjects such as Science, Technology, Engineering and Mathematics as they progress in their studies, there is evidence that factors such as generalized gender stereotypes and an educational environment that It affects girls ‘confidence more than boys’ are part of the problem. We must also highlight those models that use AI to promote a change for the better.

A tangible intervention to address these problems is the Longitude Explorer Award , which encourages secondary school students to use AI to solve social challenges and work with AI models. We want young people, especially girls, to realize the potential of AI for good and its role in driving change.

Strengthening the preparation and confidence of young women, we can change the proportion of people who study and work in AI and help address the possible prejudices of artificial intelligence.

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Apart from all the areas of application of robotics, which are many, the research places special emphasis on social robotics and robotics associated with medicine as specific development areas in order to highlight substantial impacts, both on health as in society, which will bring overcoming these great challenges or challenges.

Finally, one of the challenges is related to responsible innovation and how ethics and safety should be carefully considered as we develop the technology.

The study collects major obstacles not yet resolved in robotics. These pressing challenges were compiled during an open online survey and restricted by a panel of experts, led by Guang-Zhong Yang, director of the Hamlyn Center for Robotic Surgery at Imperial College London (United Kingdom).

Four of the challenges are related to the development of technology that will reshape the future of robotics, going beyond gears and motors. These include the creation of new materials and manufacturing methods; using brain-computer interfaces to increase human capabilities; development of low-cost but long-lasting batteries and energy collection schemes; and use nature as inspiration, either by translating biological principles into engineering design or by integrating living components into robotic structures.

Let’s look at all the challenges that robots face.

The laws of robotics

Isaac Asimov established in his science fiction novels half a century ago that: ‘A robot will not harm a human being or, by inaction, allow a human being to suffer harm. A robot must obey the orders given by human beings, except if these orders come into conflict with the 1st Law.

Crucial challenges

The laws of robotics fall short of reality. Thus, if we manage to overcome these challenges in robotics that we will list now, we will be sure that it will have a huge scientific, political and socioeconomic impact on our society in the next five or ten years.

Robotics is very wide

The field of robotics is broad and covers many underlying and associated technological areas. The identification of these challenges was a difficult task, explain the authors of the study published in Science Robotics, and there are many sub-topics not listed that are equally important for future development. The list that we will review is therefore not exclusive or exhaustive, but they draw a work scenario to start working on.

First challenge: New materials

The first challenge of robotics is to achieve innovative materials and manufacturing schemes to create a new generation of multifunctional robots, energy-efficient, compatible and as autonomous as biological organisms.

Second challenge: Biohybrid and bioinspired robots

The second challenge is to overcome not only the manufacture of biohybrid and bioinspired robots, on which we are already working but to make them work like natural systems; that is, transfer the fundamental principles of living beings to engineering design rules or integrate living components into synthetic structures to create robots that function as natural systems.

Third challenge: New sources of energy

It will be crucial to develop new energy sources and battery technologies to move these machines. Through new battery technologies and energy collection schemes, we will solve the problem of lasting operation for future mobile robots.

Fourth challenge: Robot swarms

The swarms of robots will allow the simplest and least expensive modular units to be reconfigured in a team, depending on the task to be performed, while being as effective as a larger and specific monolithic robot for each task.

Fifth challenge: Navigation and exploration in extreme environments

Robotics scientists will also have to create machines capable of navigating and exploring in barely known extreme environments, such as the deep sea. In these hostile environments, it will be crucial that robots have the ability to adapt, recover from failures.

Sixth challenge: artificial intelligence

Learning to learn is one of the fundamental aspects of artificial intelligence applied to robotics, as well as advanced pattern recognition and model-based reasoning, as well as trying to conceive intelligence with common sense.

Seventh challenge: Brain-computer interfaces

In the field of biomedicine, advancing in brain-computer interfaces (BCI) will be crucial, since neuroprosthetics, functional electrical stimulation devices and exoskeletons will have to be controlled without problems.

Eighth challenge: Social interaction

Social interaction, which includes human social dynamics and moral norms, will be another important challenge that we will have to overcome. The fact that robots can truly integrate with our social life, showing empathy and natural social behaviours is one of the most incredible and expected challenges for robotics enthusiasts.

Ninth challenge: Medical robotics

Medical robotics is another goal. With increasingly higher levels of autonomy for machines, legal, ethical and technical challenges must also be considered, as well as developing a micro-robotics that addresses the real demands in medicine. Always without forgetting ethics and legality.

Tenth challenge: Ethics and security

Ethics and security for responsible innovation in robotics. We cannot ignore the fact that in all robotic innovation both premises must be present: ethics and security. According to experts, they should be applied in social policies and norms as soon as possible, while technologies are still primary.

Solve problems before it’s too late

Precisely researchers recommend that we address these complex concerns from the beginning, while technology is still in development. Likewise, we must bear in mind that we should be more concerned with human ignorance than with artificial superintelligence: “Humans, not technology, are both a solution and will continue to be in the foreseeable future,” the scientists say.

A great cultural change

Addressing these great challenges also requires great cultural change. For example, to meet the challenges of designing bio-inspired and biohybrid robots, engineers, physicists, applied mathematicians and biologists must form mutually beneficial interdisciplinary collaborations. To extract principles, understand a biological design and use biological material effectively, it is first necessary to understand that evolution is not engineering. Particularly important for robotics is the development of a synergy where biological principles inspire the design of novel robots or components, and these robots (or their parts) are used by biologists as physical models to better test the hypotheses of biological relationships structure-function

Challenges of the next decades

According to Professor of Artificial Intelligence at the University of New South Wales (Australia) Toby Walsh, in his eyes, a great challenge in the near future of robotics could be to build a robot to discover life on the moons of the solar system or “Enter a teenager’s room, pick up clothes from the floor, wash it, fold it and put it in the closet.”

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As the firm says in a statement, this small circular device is partially introduced into the liquids you want to study. To perform the analysis and detection of the fluid, use artificial intelligence and a set of multi sensitive electrochemical sensors, each consisting of pairs of electrodes.

Complex liquids contain many different molecules and it is the combination of all of them that distinguishes them from each other. Therefore, in order to identify them, it is necessary to analyze their molecules as a whole.

Just as the sense of taste or smell does not have a receptor for each molecule of food, but reacts from a specific combination, Hypertaste sensors are able to respond simultaneously to different chemical compounds. Thus, through the combination of these different sensors, a holistic analysis of the set of components of a liquid can be performed and its ‘fingerprint’ can be found.

All the data collected is transferred through a mobile application to the cloud, where an automatic learning algorithm compares this fingerprint with a database with information on known liquids. The algorithm determines which liquids in the database are chemically similar to the liquid being investigated.

A key aspect in this whole process is the ‘training’ that the Hypertaste machine learning algorithm receives, which teaches you to recognize the characteristic pattern of voltage signals of a specific liquid, by multiple measurements of that pattern.

Among the applications of this new technology are the pharmaceutical and health sector, in addition to those related to the quality of river or lake water. It could also be used to verify the quality of certain products.

According to the creators, in the future, Hypertaste could even detect the fingerprint of other even more complex liquids. In the long term, it could, for example, take urine samples from a person and help to obtain an evaluation of the metabolic fingerprint, which can be understood as the sum of all the small molecules present in a living organism. As this chemical information is constantly changing (depending on factors such as lifestyle or nutrition) this metabolic fingerprint could help to have a ‘snapshot’ of a person’s health at a given time.

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