Artificial Intelligence (AI) is a powerful tool and its growing capabilities can be used to tackle an immense number of problems across all industries, from cybersecurity to manufacturing to healthcare. But while AI is profoundly changing the way businesses operate, concern is mounting around the inherent biases that the technology can amplify. And for good reason; today, we still see new issues with biased AI emerging, which suggests that too many organizations are still experimenting with AI without an adequate focus on ethics and potential biases.

All organizations exploring the technology should invest in an ethical framework to develop AI policies, regulations and business best practices that are environmentally sustainable and socially preferable. Together, these parameters will help to ensure that AI’s effects will be beneficial, fair, and felt by the largest number of people possible.

Diverse Teams

As organizations increasingly experiment with and rely on artificial intelligence, teams not only need to be trained on the technology but also on the potential ethical repercussions of AI deployments.  Development teams need to learn to identify sources of bias in data and to check AI models for fairness. Where possible, developers should attempt to work with simple interpretable models, which also solve issues around explainability and sustainability. Where the use case demands complex black box models, special care must be taken to avoid model cheating (learning artefacts instead of solving the problem) and extensive testing is required to detect any issues around bias and fairness.

Business managers need to learn to address ethical issues in their business cases and build in pre-deployment checks and post-deployment mitigation to address the unavoidable errors AI deployments will have. AI deployments result in special post-deployment responsibilities for continuous monitoring because even if all pre-deployment checks have been done, changes in data or people behavior can result in new unforeseen issues.

Having teams with diverse views, experiences and backgrounds working on your AI projects is essential in increasing your chance for producing fair AI. The unconscious biases we all have are amplified in echo chambers of non-diverse teams. If it’s a challenge to stand up sufficiently diverse teams, specially trained team members who take on the role of an AI ethicist can help. These employees are trained in AI technology and the ethical, legal and regulatory ecosystem. They should be pulled in already at the project design stage, be involved in regular peer review throughout the project, and have a key role in the final sign-off before deployment. They will work with disciplines across the business towards goals throughout the product life cycle, and ensure potential biases are tackled from diverse perspectives and at the earliest stage possible. AI ethicists can inform leadership at all levels about unintended non-technical aspects of AI and the impact this might have on corporate risk tolerance.

We are already seeing this trend gaining traction, with leading tech companies – even traditional ones, consulting firms, and even the U.S. military rushing to add governance to navigate the potential ethical pitfalls of AI.  The more that organizations rely on artificial intelligence, the more vital the role a Responsible AI framework plays in ensuring that the use of such technologies aligns with the organization’s mission, core values, and regulatory requirements.

Establish Governance

Developers and IT teams cannot be solely responsible for the success and impartiality of algorithms and the data sets they draw from; the responsibility must start with senior leadership. Senior leaders need to understand the trade-offs in deploying automated decision making in their operation and demand that the organization has insight into the inner workings of AI models. Explainability methods are still far from mature and are of questionable value. Organizations require documented and accessible knowledge about how a deployed AI system works, how it was built, and what its limitations are. A recent report of the UK Centre for Data Ethics and Innovation reminds senior leaders that they need to be clear that they retain accountability for decisions made by their organization even in the case of an AI making them – regardless of whether the AI was developed in-house or procured externally.

Since any AI model created from data can only ever be statistically correct, it’s unavoidable that AI makes mistakes. It’s essential that mitigations – and where humans are impacted efficient complaint processes – are part of the deployment. Organizations can establish shared monitoring and reporting systems as well as processes to ensure that the appropriate level of management is aware of the performance of and other issues relating to the deployed AI. Several organizations are already working on frameworks and tools that can help establish governance and best practice workflows for machine learning. IBM, for example, published an open source AI Fairness toolkit.  However, tackling bias cannot be simply delegated to software. Research shows that are many different possible definitions of fairness and that it’s impossible to satisfy all at the same time. This means AI developers and managers need to be aware of resulting impacts and trade-offs when addressing fairness in their products.

The key for all organizations is that defining AI governance needs to be done carefully and with significant thought. By creating a robust framework of governance, AI can be implemented consistently and safely, allowing organizations to successfully deliver AI programs responsibly, every time, and at scale.

Educate Employees

AI-centric roles must be competent in understanding its limitations, being able to identify where weaknesses or biases in the data may be hiding and recognize how to correct them to ensure that the decisions being made by algorithms are consistent with the values of the organization. This requires employee training on the organization's ethical AI framework, and could take the form of online training modules, virtual seminars or workshops.

Mandatory training sessions should bring together different departments and functions to share AI-related issues from various perspectives. For example, an HR compliance officer will bring just as an important perspective as a data engineer or a regulatory affairs officer. Sessions should train employees on how to uphold the organization’s AI ethical commitments, as well as ask the critical questions needed to spot potential ethical issues, such as whether an AI application might lead to exclusion of groups of people or cause social or environmental harm.

Time must be invested to develop and disseminate responsible AI principles, but through training, organizations can ensure that their employees are operationalizing the ethical AI framework in their day-to-day work.

Consistently Review

Effective integration heavily relies on regular assessment of the risks and biases associated with each use case. An ethical AI framework must include internal and external checks to ensure equitable application across all participants. For example, best practice ML Ops pipelines will monitor AI models in production in order to detect data drift, unwanted bias or decrease in performance. Combining ML Ops with strong data governance and data quality management can help to detect discriminatory bias and inconsistencies in the data before it’s used in machine learning processes. Embedding continuous and rigorous testing in the ML engineering workflows guards against the risk of releasing flawed AI models.

The development of AI holds the potential for both positive and negative impact on society, and charting the most responsible course will depend on the use of a framework that delivers an ethical AI strategy. Businesses can then clearly explain how they use data, monitor AI activities, arrive at decisions, and control unwanted scenarios. Ultimately, the right level of structure and control will protect customers and stakeholders and build trust in the business’s use of technology.

BT Global Head of AI & Research Dr. Detlef Nauck

Even before the coronavirus pandemic, artificial intelligence (AI) was already growing at a fast rate. For instance, IDC predicted in 2019 that the spending on AI technologies would grow three-fold by 2023. With the coronavirus pandemic, the potential of this technology in healthcare and other industries has become apparent. In just about six months of 2020, a rapid digital transformation saw companies shifting and adopting different digital technologies, including AI, in a short time. Even after the pandemic, this is not likely to stop, mainly because AI has proven to reduce the cost of operations and accelerate service delivery.

Below are some top trends you should watch in AI this year.

• Demand for AI talent will continue to be high

As AI continues gaining fame, talent will be required to accelerate adoption. This will specifically be the case going into 2021.  Businesses will continue realizing the importance of AI and how the right talent will help them succeed in their operations. Like the way Chief Data Officers (CDOs) have worked to ensure data is accessible to users, including non-technical ones, AI professionals will help democratize artificial intelligence. These professionals will work to ensure that AI is usable by different types of users. The democratization of AI has already started, with the primary aim being to contextualize, manage change and establish proper AI governance. This is expected to move a notch higher this year.

• There will be a competition for AI-enabled chips

Artificial intelligence is coming with new demands of more processing power than ever as AI systems tend to involve a lot of data processing. With such a growth in processing demand, there will be a heightened need for access to more powerful hardware. Going into 2021, AI-enabled chips and supply chains is set to be one of the trends in 2021. This hardware will increase efficiency and enable effective and widespread automation. Already, the leading chipmakers such as NVIDIA, Qualcomm, and Intel are rapidly developing AI-enabled chips in anticipation of future trends.

• Augmented processes will emerge

Artificial intelligence and data are crucial to modern organizations in gaining a competitive advantage. These two will be part of a bigger strategy for innovation and automation in 2021. Companies will seek to use AI and big data to build scalable, lean and timely data from heterogeneous sources that support innovation. Businesses will be going a step further with process augmentation. This approach will improve collaboration and enhance collective intelligence.

• Growth in AI applications

With all the talk about the use of AI becoming louder across the world today, it is my guess that 2021 will be a big year for AI applications. From retail to supply chain, you should expect a rise in the use of AI to gather insight, enhance the efficiency of operations and save cost in a digital landscape. AI will continue entering organizations through applications such as chatbots and other software platforms meant to streamline processes. Expect more interaction with AI in your mobile devices and computers as you perform your operations, such as online shopping.

• AI ethics and standards will be a leading focus

Although artificial intelligence has proven to be helpful already, there is a concern about its use. In 2021, there will be a rise in attempts to ensure organizations leverage AI against major global challenges while ensuring it meets the ethical thresholds, inclusion, and diversity. AI must also stimulate innovation and economic growth. Fairness and transparency will become the two key areas of focus as artificial intelligence become crucial for different industries. There will be ethical concerns about deepfakes, algorithmic bias, data protection, and acceptance use, that are likely to be areas of concern in 2021.

“We know how we want our companies to work.  Enterprises ought to be customer-focused, responsive, and digital.  They should deliver to each employee and customer exactly what they need, at the moment they need it.  The data and technology to do this are available now,” declares artificial intelligence expert Seth Earley.  Nonetheless, AI continues to stumble when it comes to making this a reality.  In his new book, THE AI-POWERED ENTERPRISE:  Harness the Power of Ontologies to Make Your Business Smarter, Faster, and More Profitable (LifeTree Media/April 28, 2020), Earley provides practical approaches for solving the data management problems that are at the heart of this disconnect, showing how organizations can truly deliver on the promise of AI. 

“To create transformative AI solutions, we need a holistic, synergistic, and simultaneously integrated flow of information,” explains Earley, who has helped companies across industries to manage data to enable digital transformations.  The problem is that, all too often, this foundational principle is ignored, given short shrift, or deprived of resources. 

The answer, Earley says, lies in ontology – a consistent representation of data and data relationships that can inform and power AI technologies.  In other words, ontology is “the master knowledge scaffolding of the organization.”  Without it, any AI-driven transformation will be slow, costly, and less effective.  

In language accessible to a non-tech audience, Earley draws on examples from numerous client companies to describe what correct execution of data management looks like.  He addresses how to manage this transformation, step-by-step, covering such issues as:

Customer Experience – Customer experience is hard to get right and easy to get wrong.  “It’s a question of the proper integration of technology,” writes Earley.  He explores the roadblocks that hinder a systematic approach to customer experience, and offers a solution:  a “high-fidelity journey map” that accounts for how technology represents and enables elements of the customer experience. 

Marketing – Good marketing is about presenting the right content at the right time to engage the customer.  Today, this means reading the online equivalent of physical body language: “the digital breadcrumbs, cues, and clues that tell us about what our customers need, how we can meet those needs, and how to best present the content most likely to engage the customer at that moment in their journey.”  According to Earley, digital marketers must become knowledge enablers, champions of data quality, architects of digital systems, and keepers of the ontology that powers it all. 

Ecommerce – Ecommerce is where ontology-powered AI can have its biggest impact, says the author.  Its success or failure depends on the quality of data.  THE AI-POWERED ENTERPRISE addresses how to enhance this quality by improving both customer classification and product taxonomies (categorizations) based on features and relationships.

Sales Process – “AI technologies can improve every part of the sales process by freeing sales staff from routine tasks and making them more efficient,” Earley writes.  He discusses effective use of AI-powered chatbots in customer interaction; machine learning to train AI systems to identify sales prospects; and sematic search to recommend the most productive approaches to sales leads.

Employee Productivity – AI and efficient knowledge retrieval systems can enhance productivity across a range of tasks.  The author describes how to overhaul knowledge management systems and text analytics so they can be used in making hiring decisions, training employees effectively, and providing them with access to the correct, contextualized information in their day-to-day work.

In addition to these issues, Earley explains how having the right ontology and data structures can enable AI to improve supply chain dynamics and logistics – and can even have powerful impact on strategy and governance issues.  Moreover, he outlines the basic principles that should guide leaders who are undertaking digital transformations of their organizations.

“The winners and losers of the next fifteen years will be determined by who best harnesses AI for solving business problems for employees and customers,” Earley contends.  Combining a sophisticated explanation of how AI works with a practical approach to applying it to a range of business problems, THE AI-POWERED ENTERPRISE is a must-read for CEOs, CMOs and technology executives – along with anyone who wants to understand the role of AI and how to get a jump on the opportunities it presents.

SETH EARLEY is CEO of Earley Information Science (EIS), a leading consulting firm focused on organizing information for business impact, with expertise in knowledge strategy, data and information architecture, search-based applications, and information findability solutions.  He is currently on the editorial board of the Journal of Applied Marketing Analytics and is a former Data Analytics department editor for IEEE’s IT Professional magazine. He lives in Carlisle, Massachusetts.

For quite some time now, pressure has been mounting in the AI industry for tech companies and big conglomerates to wrestle control over its diversity crisis. From home assistants that can remind us to do chores and look up information on demand, to customer service chatbots that take care of queries and complaints, we are increasingly relying on technologies that use AI to assist in our daily lives. In the months and years to come, the reach of these technologies is likely to extend even further, and as such the conversation around their ethics has recently come to something of a crescendo.

With organisations like Women in Machine Learning and Black in AI acting as torchbearers for underrepresented groups in the AI industry, progress has certainly been made to foster diversity and inclusion over the past few years. But unfortunately, these wins are not the end of the story.

Recently, the alleged firing of prominent AI ethicist and co-lead of Google’s Ethical Artificial Intelligence team, Timnit Gebru, has sparked further debate. After a week of disputes over the company’s request to rescind a paper that she had co-written, Gebru left the team. And although Google maintains that the sacking was, in fact, a resignation, it should be noted that the paper in question was none other than one contending that companies must do more to guarantee that AI systems do not enact historical gender biases and offensive language.

Office politics aside, Gebru’s story is yet another pertinent reminder that bias thrives in our conferences and community, as well as in society at large. Put simply, when the leaders and decision-makers in the AI community do not accurately reflect the diversity of society at large, this presents significant issues. Whether Uber’s failure to recognise trans drivers, or sentencing algorithms that unfairly discriminate against black defendants, it is clear that more work must be done behind the scenes to remedy diversity standards within the industry before these errors become too difficult to correct.

If we are to build pioneering tech that works for all and serves all, one thing is for certain: this work must begin in classrooms and university seminar rooms, as much as it should be involved in the development of the tech itself. The question now is, what can companies do to help?

Taking steps towards inclusivity

It probably goes without saying that there is no silver bullet for improving diversity standards, and that meaningful change will be a steady and incremental process. But there are a number of things business leaders in the AI space can do to hurry things along.

From a purely technical standpoint, we must remember that AI is not implicitly biased; that’s why detection tools and white papers must be continually published to ensure that toolsets do not exacerbate pre-existing diversity problems, or put unrepresentative data to use.

Organizations however, can take the first vital steps towards fostering an inclusive company culture, and a workplace where all voices can be reliably heard. If an employee does not believe that a particular product or practice within the workplace reflects appropriate diversity standards, then they should be able to say so, so that teams can hold themselves to account. Above all else, this is the right thing to do – but it should not be overlooked that a diverse workforce can help companies perform better commercially, too.

More specifically, companies should also consider their employment practices. Asking important questions such as: are loyal employees from diverse backgrounds being recognized for their hard work? Organizations that have the means to promote these employees should do so. After all, if the students and professors of the future are able to see themselves in the leaders of today, then they might be more likely to consider a career in AI.

Beyond in-house changes to policy, efforts towards improving education and employment practices must also be bolstered if we are to benefit from AI on a level playing field and create a generation of strong role-models. To shift the status quo, Governments and state-led enterprises must take note and increase funding into STEM education and outreach.

Educational institutions, too, must conduct more research to address the nuances of how identity shapes students’ relationships to tech and the computer sciences. In this way, the benefits to society will be two-fold; not only will individuals from all walks of life be able to contribute to ground-breaking research at elite institutions, but society at large will be able to profit from the development of truly inclusive AI.

Ultimately, the future of the AI space is in our hands. Companies and Governments alike must take the necessary steps to ensure that the technologies of tomorrow are built to transcend geographical borders and cultural limits. Once we as an industry widen the scope of enquiry to examine how tech works in context with more rigour, and companies follow suit, I am confident that we will all be able to benefit democratically from the many benefits that AI has to offer.

Nikolas Kairinos is the chief executive officer and founder of Soffos, the world’s first AI-powered KnowledgeBot. He also founded Fountech.ai, a company which is driving innovation in the AI sector and helping consumers, businesses and governments understand how this technology is making the world a better place.

Researchers working on neural nets are discovering how the brain learns, reports Quanta Magazine. 

In 2007, some of the leading thinkers behind deep neural networks organized an unofficial “satellite” meeting at the margins of a prestigious annual conference on artificial intelligence.

Read the article on Quanta Magazine

Researchers have used artificial intelligence to decode DNA, reports Scientific Computing. 

A German-American team of scientists have deciphered some of the more elusive instructions encoded in DNA with the help of artificial intelligence (AI).

Read the article on Scientific Computing

According to St. George News, artificial intelligence can help people combat loneliness brought on by the pandemic. 

In the isolation of the COVID-19 pandemic, many people are missing a sympathetic ear. Would a response like that make you feel heard, less alone, even if it were a machine writing back to you?

Read the article on St. George News

According to a new study artificial intelligence can influence the human decision making process, reports ZD Net.

A new study by researchers from the Commonwealth Scientific and Industrial Research Organisation's (CSIRO) Data61, in partnership with the Australian National University and researchers from Germany, has revealed that artificial intelligence (AI) can influence human decision-making. 

Read the article on ZD Net

Artificial Intelligence is the next frontier of digital transformation. Businesses and governments are now rushing to invest millions of dollars as they seek to stay ahead of their competitors. The year 2020 was the year that the role that AI can play became apparent. It had a far-reaching impact on health and safety, where it played a crucial role in saving human life in various ways in healthcare and enhancing economic resilience. As we start a new year, AI is set to guide 2021 efforts in many ways.

Here are some few areas that AI is expected to help in 2021:

• Vaccine development

With the coronavirus still spreading fast, the focus of the entire world is in the development of vaccines. Although some vaccine candidates have become successful in human trials and are in use, the hunt continues. The high-speed vaccine development was partly due to advanced technologies, AI being one of them. AI models aided researchers in the analysis of large volumes of data on different coronaviruses. With tens or even hundreds of thousands of components of data about the outer proteins of a virus out there, machine learning models can help sort through this massive data and predict immunogenic protein components. This will guide researchers in developing vaccine candidates.

• Self-driving cars and robotaxis

Despite challenges in other industries, 2020 was a good year for self-driving vehicles and robotaxis. Industry’s leading companies tested driverless cars, with some opening up robotaxi services in cities. While these technologies have not fully matured, 2021 will be a good year to yet again go a notch higher and mature. Companies such as Baidu already have Apollo Go Robotaxi service in Changsha, Beijing and Cangzhou and other players might soon follow suit. Advancement in AI means that self-driving cars and robots will become more sophisticated and have more autonomy than ever.

• Quantum computing

The race for quantum computing is on. With the help of AI, it is making significant inroads, and 2021 might see major breakthroughs in quantum computing supremacy battles. Companies such as Google, IBM, Baidu, and Accenture show a relentless effort to capitalize on AI to build quantum computing. Advanced AI technologies such as deep learning algorithms are also becoming crucial in developing quantum computing research as the race heats up. Baidu, for example, made breakthroughs in 2020 that bridged AI and quantum computing.

• AI chips

Many companies lead when it comes to the manufacturing of AI processors. This is an area that is the attention of tech giants such as AMD, Intel, and Google. While the ordinary processor can support AI tasks, AI-specific processors are specially designed to optimize tasks. As AI applications increase and become widespread in different industries, an increase in performance will allow companies that operate data centers to unlock more value and enhance operations. Google, for example, is currently working on AI-powered Edge TPU which has already grabbed attention due to its potential to enable high accuracy and capability.

In conclusion, 2021 is a good year for artificial intelligence. It is the year where competition among the big boys is expected to rise as every company or country wants to become the leader. Regardless of who wins or loses the race, the bottom line is that AI will benefit all of us irrespective of the industry we fall. Industries such as healthcare, real estate, supply chain, research and development are expected to reap a lot from artificial intelligence. From now on, we must look into how we will benefit from this technology and capitalize on it.

Although many people doubted the benefits of artificial intelligence (AI) before, this technology has proven to be capable of giving more than what many had expected.  One of the areas that appear to reap the most out of the advancement of AI is the employment sector. This comes in as the world races to contain the spread of the COVID-19 by enforcing social distancing modes such as working from home. Throughout this period, remote work and work from home have become the leading area of conversation across the industries. The availability of technology such as Zoom and the advancement of AI and virtual reality have made it more viable for different companies to adopt new means of working while the coronavirus pandemic has acted as a catalyst for this change.

Although the coronavirus pandemic has ruined many areas of the economy, one area that has benefited significantly is the work from home (WFH). WFH has been made unavoidable for many businesses and employees who still have to keep going despite the disruptions. While some employees and corporate leaders feel threatened by this, the workplace has proven to be easily manageable with modern technology. Augmented Reality (AR), Mixed Reality (MR), and Virtual Reality (VR) have shown the potential to create a real-world-like immersive communicative environment between the employers, employees, and clients. The technology can be used to conduct virtual meetings and take company tours as well as participate in virtual whiteboards. Here is a deeper look into ways in AI can help businesses to set up a WFH environment:

• Remote work management

Human resource managers carry out various tasks that ensure organizations that there is strict adherence to the legal hiring regulations and the organization’s policies. As such, the process of selecting the right candidates consumes time. However, with the advancement of technology, remote positions can be created to lighten the work of managers and employees on-site. Although critics are concerned with the ability of companies to monitor the performance and work quality of remote employees, AI and machine learning technologies will help the supervisors to monitor these aspects. These technologies provide periodic performance surveys, reviews, and accurate reports that can be used to identify areas that require improvement.

• Virtual reality will enhance collaboration

Imagine working in an environment where you are far away physically, but you feel like you are in the same office or meeting room with your colleagues. It all seems impossible, right? Virtual reality is making this happen. With just a headset and noise-canceling headphones, an employee can enter a collaborative and immersive virtual environment with others. Sensors can pick up critical aspects of humans such as body language and nonverbal communication, visible in the real world. Examples of such sensors are those found in Oculus Rift, which has more capabilities than teleconferencing platforms such as Zoom and Skype. This is precisely the reason why VR is holding lots of potential in a collaborative workspace.

• Augmented reality will add digital layers of information

Augmented Reality (AR) adds digital layers of information to the workplace’s physical environment. For companies such as those in the tourism sector, customers can visit guest rooms, pools and beaches virtually. On the other hand, the customers who want to purchase specific items are taken around virtual showrooms in an expedited immersive manner. In workplaces, AR allows employees to sit side-by-side at a virtual table with their colleagues and discuss their work as if they were together.

• Employee training with 3D modeled interaction

Mixed reality (MR) boosts the virtual training of employees through simulated hands-on experience. Since some professions such as product design, medicine, and engineering fields require intense training, mixed reality can come to the rescue. With AI-enabled mixed reality headsets, different activities can be simulated to employees during training. This can even be an improvement to the traditional training environment, which mostly entails watching videos. MR allows realistic training experience for employees without exhausting actual resources. This will cut employee training costs and allows workers to test various scenarios without damaging a thing.

In a nutshell, MR, VR, and AR present a massive potential that, if well utilized, can see the evolution of remote work from what it is today to something that we will all be proud of in the future. With the coronavirus pandemic, all companies must find alternative ways of ensuring work goes on even during disruption, and these three presents the right alternative.

Like several other banks in the EU reeling from the impact of the 2009 financial crisis, our client Bank (name withheld for confidentiality) had to take measures to restructure the non-performing loans under a parliament-approved securitization scheme. The Bank uses its in-house call center to contact non-performing loan debtors and negotiate offers to restructure the debt according to the debtor’s unique situation and their ability to pay-out the loan.

Given the effect of non-performing loans (NPLs) on its bottom line, the Bank was seeking a technology partner to help it optimize the collections process. Behavioral Signals partnered with this Bank and applied its AI-based agent-customer matching technology towards maximizing the effectiveness of their Call Center. 

AI and Finance

Financial institutions are leveraging AI systems in their contact centers for improved efficiency in delivering service to customers, helping them engage the customer uniquely. The ability to route and measure agent-customer interaction offers not only a higher probability of achieving the desired result, be it a sale or collection, but also insights on how AI is impacting the whole communication process.

Behavioral Signals  proposed a model to optimize the call centers’ outcomes by using machine learning techniques to route the  caller to the best-matching agent. By creating agent and customer behavioral profiles, from their past audio interactions, we were able to create a predictive model on which agents’ skills and overall behavioral traits during the calls are best suited for the specific customer in order to achieve the desired result.

From Voice to Revenue

The solution was deployed on Live-Data in a champion/challenger setup demonstrating a significant return-on-investment with an actual increase of active debt restructuring applications by 20.1% in total. On top of that, this improvement was essentially achieved with 7.6% fewer calls (Right Party Contacts), leading to additional cost reductions. In absolute numbers, these results actually correspond to approximately $7.5M USD of additional restructured debt for the Bank over the course of this evaluation and a potential $300M USD annual upside.

Next-Gen Banking

Contrary to a common concern, this drastic improvement of collection outcomes did not compromise the customer’s satisfaction levels. The Bank noted that these interactions have been smoother than average with the majority of interactions rated very high by the Bank’s QA team, clearly underscoring an improved ability of the agents to build rapport with their clients. Furthermore, these interactions provide excellent examples of successfully-handled interactions that can be used in training/ coaching of new and existing agents, thus improving the performance of the call center as a whole.

The Bank is extending the scope of cooperation with Behavioral Signals to deploy the Behavioral Profile Pairing (agent customer matching) application to the entire debt collection team along with other applications, to improve the efficiency and performance of its call center. 

It has now become common hearing people talking about how artificial intelligence (AI) will change various aspects and industries in every country on the planet. There are growing fears that this technology will eliminate many jobs and professions through automation of tasks. Many reports have shown the truth behind job automation especially for certain professions and demographics. For example, a report by Brookings Institution noted that automation threatens more than 25% of jobs in the US, most done by low-wage earners where tasks are repetitive and routine-based.

Despite these harsh realities, we have no option but to conform to the new changes and adopt these technologies due to the massive opportunities presented that far outweigh downsides. Although artificial intelligence will indeed make other tasks obsolete, it will also inject more jobs and change the existing ones for the better. Workers in different countries are now beginning to see the bright side of AI on their roles, and organizations and are slowly starting to change their minds on this technology. Most of them are now grateful for having robots as their coworkers because of how they simplify and streamline their lives.   

A majority of human workers now trust orders given by robots over those of human managers. Most of them are even turning to robots for advice instead of their human managers. In some companies like American Express, for example, tasks such as identifying product offers that are relevant to a specific customer segment are now made by AI, therefore taking over the role of managers and employees in handling these tasks.

AI is now taking over administrative tasks traditionally done by human managers while the roles of managers are now evolving as they focus more on soft skills over hard ones. Surveys have indicated that robots are far much better than human managers in doing tasks that require hard skills as they can easily give unbiased information, maintain work schedule, solve problems quickly and their budgets can also be easily managed. On the other hand, humans can complement them with better empathy. Unlike humans, robots can also provide answers to confidential information without causing fear among the interviewees. They can also evaluate performance of the team with no bias. Although robots and AI systems have advanced significantly, human managers still beat them in many areas. For instance, human managers can understand feelings of other humans better and can reliably provide oversight and direction.

Artificial intelligence collaboration with humans in workplaces is freeing up managers, giving them time to focus on other issues of their jobs. By cutting managers loose from their traditional tasks, AI is enhancing innovation. They allow managers to have more impact on their work by giving them time to create strong relationships with other employees that in the end impacts their roles.

Far from replacing managers, AI is improving their decision-making capabilities. The main change in decision making in management originates from the AI’s deep learning abilities. Machines through deep learning are becoming smarter with time as they continually learn from the experience of human managers just like the way managers gain their knowledge from interacting with other managers and encountering new challenges. With deep learning, AI promises to be part of decision making process in the coming days. Through the help of AI, managers can tap into the advice they can offer at any time due to large data they have. This will make managers smarter and more qualified to make informed decisions that can take a company to the next level.

Machine learning systems are the crux of most of the major AI advances over the last decade.  Deep learning, the most advanced type of machine learning, has produced the computer vision systems that power self-driving cars, facial recognition systems, machine translation, speech recognition, and AI-based medical diagnosis systems.

But can we trust them?  The goal for self-driving cars is to drive more safely than human drivers.  I want assurance that, if the human would stop for a baby stroller in the road, then the car will stop for that same stroller.

Unfortunately, we'll never be able to completely trust deep learning systems to avoid that stroller.  Similarly, we'll never be able to trust cancer diagnosis systems to detect every cancer that a human doctor would detect -- even if the deep learning cancer detection system does better overall than a human doctor.

Let me explain why...

A deep learning system identifies patterns in its training data that enable it to do a particular task.  For example, if I want to train a deep learning system to distinguish pictures of cats from pictures of dogs, I can give it a large set of training examples that include pictures of cats and dogs.  Each picture would be labeled as a cat or dog.  The deep learning system will identify the patterns in the data that best distinguish the cats in the training set from the dogs in the training set.

The fine print here is "in the training set".   If all the pictures of dogs are outside in the training set, and all the images of cats are inside homes, the deep learning system will likely key in on yard and home features. Then if I show the system a picture of a dog inside a home, the system will probably label it a cat.

Similarly, in the figure above, before researchers pasted the guitar onto the picture, both computers and human subjects correctly labeled the monkey in the photo. Adding the guitar did not confuse people, but it made the computer think it was seeing a picture of a person instead of a monkey. The computer did not learn the visual characteristics that people use to recognize monkeys and people. Instead, it learned that guitars are only present in pictures of people.

Researchers have also found that deep learning systems make similar mistakes in machine translation, music content analysis, question answering, sentiment analysis, topic identification, and many other types of deep learning systems.

The problem for deep learning systems is that the data patterns in the training are almost never exactly the same as the data patterns found in the real world.  The result is that deep learning systems make surprising mistakes that would not be made by their human counterparts.

What does this mean for self-driving cars?  The machine learning system in a self-driving car will only detect baby strollers in the road to the extent that the road, strollers, and other conditions are similar to examples in the training set.  When an Uber self-driving test car killed a pedestrian in 2018, the car’s deep learning software first classified the pedestrian as an unknown object, then as a vehicle, and finally as a bicycle.  I don't know about you but I don't want self-driving cars on the road that might fail to recognize me as a pedestrian or might fail to stop for a baby in a stroller. 

Deep learning technology offers great benefits to society.  But we need to be smart about where and how to put it to work.

STEVE SHWARTZ is a successful serial software entrepreneur and investor. He uses his unique perspective as an early AI researcher and statistician to both explain how AI works in simple terms, why people shouldn’t worry about intelligent robots taking over the world, and the steps we need to take as a society to minimize the negative impacts of AI and maximize its positive influence. 

He received his PhD from Johns Hopkins University in Cognitive Science, where he began his AI research, and also taught Statistics at Towson State University. After receiving his PhD in 1979, AI luminary Roger Schank invited Shwartz to join the Yale University faculty as a postdoctoral researcher in Computer Science. Learn more about Steve Shwartz at www.AIPerspectives.com and connect with him on Twitter, Facebook and LinkedIn. His new book, Evil Robots, Killer Computers, and Other Myths: The Truth About AI and the Future of Humanity, will be available on Amazon and other major booksellers in February 2021. 

Although 2020 did not go exactly as expected, 2021 seems encouraging for many industries. For Machine Learning (ML) and Artificial Intelligence (AI) at large, experts predict that it will enter a golden age this year. With machine learning now top among the most sought after technologies, here is how machine learning will advance in 2021:

• More organizations will prioritize machine learning in their IT budgets

Despite the disruption occasioned by the coronavirus pandemic, 2020 was a good year for AI and its applications. The year saw organizations increasing their adoption to enhance their performance. According to a report by Enterprise Trends in Machine Learning, organizations are expanding their ML projects into a wide range of applications such as process automation and those that will improve customer experience. The study found out that 76 percent of companies will prioritize ML over other IT projects in 2021. The pandemic was responsible for the sense of urgency regarding the adoption and completion of AI and ML projects.

• Augmented analytics will transform businesses

Augmented Analytics uses artificial intelligence and machine learning technologies to assist in preparing data into meaningful information. This technology has proven to be crucial in assisting organizations in scaling their AI practices. As organizations continue looking for ways to optimize their workflows, most of them will ask their business intelligence (BI) teams to develop ML models to help them achieve this. Expect a new class of developers (AI-developers), who will develop new platforms to help BI teams in their operations.

• The number of data scientists will continue rising

Data scientists are an important part of AI and ML. As such, the demand for data scientists is higher than ever. This has led to a rise in the number of data scientists from last year. Between 2020 and 2021, the number of data scientists rose to 76%. This demand is expected to rise this year as organizations implement big data projects. AI and ML projects are driving the demand for skills at a 71% compound annual rate between now and 2025.

• ML and AI will trigger innovations

The next wave of digital transformation will be powered by artificial intelligence. This innovation will aim at reducing inefficiencies in organizations, generating data-driven insights, and automating business decision-making. These innovations will advance from “early adopters” such as organizations in the financial services sector such as insurance and manufacturing, to other industries. Expect machine learning to be embedded in more business functions across different industries to enhance efficiency. This will create new products and services that will increase income.

• Ethics, explainability, and interpretability will be important

As machine learning becomes increasingly adopted, ethics and explainability will form a crucial part of it. This will lead to a demand for more regulation, privacy, and more transparency to build trust. There will be a rise in concerns and risks posed by ML/AI models and automation. Interpretability will also form a crucial part of the ML/AI project’s decision-making. It will help organizations stay accountable for their actions and decisions regarding their projects. Therefore, as we move forward, expect more regulatory frameworks to guard privacy and improve understanding of AI projects.

In a nutshell, ML projects will increase in scale and sophistication from now on. As such, large corporations will hire Chief Analytics Officers (CAO) and invest in resources to leverage analytics. With this, AI companies will need to be more transparent about collecting, storing, and using people’s data. They must be mindful of how they use data fairly and equitably. Only then can an organization make the most out of AI and ML and deploy this technology to enhance production.

 Machine learning (ML) is altering the landscape of all segments and industries such as education, transport, healthcare, and entertainment, among others. It will impact operations in other areas such as housing, shopping and cars. This technology is used in robotic process automation and making predictions that give business decision-makers insight regarding operations. As machine learning technology continues advancing, here are some trends that prove that it is changing:

• Regulation of data

The increase in the number of mobile devices has resulted in massive production of data. This is the reason why data has become an essential resource for any organization. The bigger challenge here, however, is ensuring that the data is relevant. With such a massive amount of data from diverse sources, there is a need for data to be sorted in terms of type and quality. From now on, expect an increase in the number of cloud solutions and data centers set up for this purpose to increase. Machine learning will be used in these data centers to categorize data as a way of enhancing efficiency.

• Big data is intersecting with IoT

The internet of things (IoT) is another area that is quickly developing. According to Transforma Insights, more than 24 billion IoT devices will be produced by 2030, generating an income of more than $1 trillion. Machine learning is becoming interlaced with IoT to make IoT devices smarter and secure. This collaboration is advantageous to IoT, and AI as these devices generate massive data needed by both AI and ML to work effectively. There will be a stronger link between IoT and ML that will see devices becoming more sophisticated in the future. For example, in manufacturing plants, IoT networks can gather data that can be analyzed by machine learning algorithms to improve production and performance.

• Marketing with the help of Machine Learning

Marketing is an important element in any business. It is only through effective marketing that a company can be able to withstand tough competition. It makes customers aware of a given brand and enhances visibility while ensuring that a given revenue target is met. With the diverse marketing platforms, it has become difficult to prove that a business is in existence. However, patterns can be extracted from user data, allowing the formulation of successful and effective marketing strategies. Machine learning algorithms are being deployed increasingly to mine data and generate patterns that are used in decision-making. This is anticipated to more than double in the future.

• Faster computing power

Industry specialists are now considering the importance of artificial neural networks, that can help solve different business problems. AI and ML, in particular, can help in decision-making, therefore improving user experience. As the year begins, more breakthroughs in algorithms are expected, increasing the ability of organizations to make more accurate decisions and at a faster pace. However, with this advancement, there will be a rising demand for privacy and knowledge on machine learning and its capabilities. This is the only way of building trust and triggering growth. This cannot be accomplished without adequate computing power, which increases processing speed. Once businesses have found the right machine learning algorithms, it is crucial to have more power centers to provide the hardware and software required for faster processing.

In a nutshell, machine learning will be functionally immense this year than it was in 2020. With the advancements in other areas such as IoT, machine learning will help handle various tasks maintaining accuracy and making faster decisions than ever before. These are important for the success of businesses, the majority of which want to satisfy their customers.

Artificial Intelligence (AI) is now part of almost every industry. From parking systems to smart sensors, AI is promising to alter the way we do things through technologies such as computer vision and machine learning, and others. This technology provides tools that can be used to revolutionize education and training institutions. Its potential in this industry is enormous, ranging from robotic teachers, personalized learning, coaching using artificial intelligence, and automating administrative tasks in schools to reduce the cost of operations and enhance efficiency. Here are some ways in which machine learning may change education:

• Personalized learning

Some countries are already taking advantage of AI in classrooms to shape the education system. Just like the personalized recommendations in Netflix, machine learning is used in teaching students at schools. Unlike the traditional technologies that do not cater to pupils sufficiently, AI learns each students’ needs, allowing teachers to perform much better. Unlike the thinking by many that machine learning and AI, in general, will replace teachers, it will position them to perform much better. Through machine learning, AI systems can learn the needs of each student and customize in-class assignments and exams and ensure that students get the best assistance necessary.

• Simplification of administrative tasks

AI can automate administrative duties for teachers and learning institutions. These are the things that teachers waste too much time on. Areas like grading exams, assessing homework, and responding to students waste time that would have been used in other things. But with AI, grading tasks can be automated. This means teachers will spend much of their time with their students instead of grading them. AI comes up with better methods of grading essays and written answers and can perform this even better than humans. Admission boards are also reaping big from AI. This technology allows automation of classification and processing of paperwork.

• AI can act as a tutor and detector of learner’s mood

Unlike humans, AI is better at reading the emotional states of students. With AI capabilities, machine learning applications can tell when the students are bored, confused, or disturbed. It can then adjust lessons based on the emotional state of students. Furthermore, AI software can help students analyze papers of other learners as part of the peer-review process. This analysis can offer additional feedback, which can be useful for students in their learning process. Over the next few years, artificial intelligence will have substantially improved how students learn and make it easy for tutors to enforce peer reviews.

• Smart content

Artificial intelligence can be enforced to ensure that students attain the ultimate academic success they need. With the sophistication of AI, smart content has become one of the hottest topics around. Technology can produce virtual content, such as video lessons and video lectures. The AI systems are using traditional syllabuses to come up with new learning interfaces to help students, regardless of their academic grades and age. An example is Cram101, that uses AI to make textbook content that is more understandable and easy to navigate. AI has led to the creation of things such as flashcards and other practical tests needed by students for learning.

• Automated grading

Understanding the way students learn and read is critical in any learning arrangement. AI can be used to achieve this and develop content in real-time. Furthermore, AI can help understand how students answer exam questions. These abilities allow teachers to automate their learning and grading and other administrative tasks. Already, companies such as Grammarly have detailed grammar feedback software that allows students to improve their writing skills.