“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.

Artificial intelligence (AI) is impacting the future of almost every industry in the world today. It has been pitched as the main driver of other technologies such as robotics, Internet of Things, big data and business process automation. Going into 2021, AI will continue to be a fulcrum of technological innovation and will be so for foreseeable future. The next few years will see an unprecedented fast development and adoption of emerging technologies driven by AI. To prepare industries on what awaits them, here are some top issues for AI in 2021.

• AI and Machine learning will lead to hyperautomation

According to Gartner, a market research firm anything in an organization, such as legacy business processes can be automated. The coronavirus pandemic has accelerated the need for automation as many companies seek to make machines do some tasks to reduce human interaction and overcrowding in workplaces to tame the virus. AI and machine learning will become key components and major drivers of automation. Technologies such as robotic process automation will depend on the advancement of these two to move to the next level. Automated business processes with the help of machine learning and AI will be able to adapt to the changing circumstances and respond to unexpected business situations.

• AI-powered chatbots

AI-powered chatbots, sometimes known as conversational AI are gaining acceptance in many organizations. They are improving reach, responsiveness and customer experience in general due to personalization. AI-powered bots are evolving into better customer service automation. They use natural language processing (NLP) and machine learning (ML) to understand humans and their needs and provide a near-human level communication. As a visitor of websites, you might have encountered these bots at some point in your life. As many things such as learning, shopping and customer support move to online environments, these bots will become a norm. This is likely to start as soon as 2021 due to the effects occasioned by the coronavirus.

• AI will be used in cybersecurity applications

Cybersecurity is one of the main areas of concern in the world today. As such, Artificial intelligence and Machine learning algorithms are showing some potential in tackling the ever-increasing cybersecurity challenges. As security challenges keep increasing, developers and cybersecurity professionals are engaged in a race to update their technology to keep up with highly sophisticated cyber-attacks and threats from malware, DDoS attacks and others. AI and machine learning technologies will be instrumental in identifying these threats and isolating them earlier enough.

• Increase in demand for ethical AI

The rising demand for ethics pertaining AI is at the top of the list in trends for 2021. According to Forrester, the next decade will require a strict response to digital acceleration and ethics in the management and use of AI. Although this topic has been left unattended for long, it is fast emerging as an area of concern due to biases that have emerged from AI projects. Consumers are demanding AI that is value-based and accountable. With time, companies will choose partners who are committed to ethics. This will see the adoption of data handling practices that reflect openness, transparency and accountability. There will always be questions around AI technology.

• IoT and AI will intersect

The internet of things (IoT) has been growing faster in recent years. It is expected that this industry will generate over $1.5 trillion in revenue by 2030, with over 24 billion devices in the market. With this growth, AI and ML will increasingly be intertwined with IoT. ML algorithms will be employed to make IoT devices smarter and secure. Also, the massive amounts of data generated by IoT devices will require ML and AI to decipher and generate patterns used to make critical decisions.

For many years, artificial intelligence (AI) was only seen on movies and fiction books and magazines. While it was like magic to the eyes, most thought it was a dream that could not be accomplished. Fast forward 2020, AI has become one of the most exciting technologies that almost everyone is talking about.  AI has moved from a hype state in tech and fiction movies to a practical usage in different industries as companies begin to take advantage its power. As 2020 comes to an end, we are already seeing countries and businesses striving to lead in the AI race. Here are some trends that have defined artificial intelligence in 2020 going forward:

• Blockchain, AI and IoT will converge

Artificial intelligence has been increasingly integrated with other technologies. This can be seen on the linkage between AI and IoT in self-driving cars. While AI models are used to help in decision-making IoT is used to collect real-time data that can help the AI systems make informed decisions. On the other hand, blockchain can aids AI systems to address concerns such as security, trust issues and scalability. Looking at 2021, these technologies are expected to help in areas such as healthcare, transport and logistics and others to ease decision making and management.

• Use of data synthesis methods

AI depends largely on deep learning and machine learning algorithms to enhance different systems and improve on decision-making capabilities. As AI technology keep improving, more pre-trained models developed using deep learning approaches will emerge. The challenge that is expected is the acquisition of data at the right time, managing it, and incorporating them into the existing systems. In 2020, artificial intelligence systems have been taking advantage of massive data sources to increase its accuracy and effectiveness in training data.

• Rising number of AI-powered devices

This year has already seen an increase in the number of AI-powered devices. This is expected to double in the coming year as companies and governments increasingly invest in AI initiatives. There is already an increase in the number of devices that enable human interface and automation. These devices will continue increasing opportunities in business which can no longer be ignored. There has been a continued development AI-enabled devices due to the rising interest in automation. As such, there will be increased elimination of time consuming tasks from now going forward. There is a rising number of AI tools ranging from household devices, cell phones, watches and other devices that will increase their presence in the market going forward.

• Healthcare adoption of AI

This year has been tough for healthcare as coronavirus pandemic stretched the industry to the limit. As healthcare look for better ways of enhancing efficiency and improving the delivery of service in general, AI has given the industry hope. There has been an increasing demand for speed that has led to the dependence of real-time data. With such data, there is a potential for real-time diagnosis. For instance, AI-powered tools can help in detection of diseases such as cancer among others. Machine learning applications have enabled better treatment and detection of diseases such as the coronavirus pandemic at their early stages, increasing the chances of saving lives significantly.

• AI is driving real-time consumer interaction

As we move towards 2021, more marketing activities are on top gear as businesses look to start the year on the right foot. With big data analytics and artificial intelligence through machine learning, organization are managing real-time customer concerns and interactions across different channels. Companies have employed AI tools to enhance their customer retention capabilities, some of which have been a challenge when using traditional methods. Apart from its ability to enhance customer interactions, AI is also aiding marketers to correctly target new audiences on different platforms such as social media and others.

Soon artificial intelligence could train your dog, reports New Scientist.

Artificial intelligence could train your dog while you are out at work. A prototype device can issue basic dog commands, recognise if they are carried out and provide a treat if they are.

Read the article on New Scientist

Artificial intelligence generated texts were able to fool Medicaid workers, reports Wired. 

In October 2019, Idaho proposed changing its Medicaid program. The state needed approval from the federal government, which solicited public feedback via Medicaid.gov.

Read the article on Wired

According to NNY 360, the Massensa Central School District is using a chatbot enhanced with an intelligent knowledge base to communicate with families in the district. 

The Massena Central School District is using artificial intelligence to better communicate with students and families.

Read the article on NNY 360

According to Sci Tech Daily, artificial intelligence researchers believe that in the future artificial intelligence machines might need to “sleep”.

One of the distinguishing features of machines is that they don’t need to sleep, unlike humans and any other creature with a central nervous system. 

Read the article on Sci Tech Daily

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.

From Super Bowl ads boasting the benefits of machine learning to rappers touting the advantages of artificial intelligence, nearly everyone knows about the cloud, how powerful it is, and the advantages it can provide for both businesses and consumers. Then you also have the pundits adding to the hype with absolute statements like this:

“If you have not developed a cloud-first strategy yet, you are likely falling behind your competitors” -Gartner

If you can find a flaw with that statement, then you’re ahead of the game.

Cloud-first is a brilliant strategy that has served as the de facto CIO direction for close to a decade. Nearly every CIO serving entities from the government to leading enterprises publicly embrace this approach. The cloud-first strategy has resulted in a predestined design where cloud-compatible data and applications find their way to the cloud to achieve efficiency and flexibility.

Cloud technologies are so powerful and beneficial that once an organizational cloud standard is in place, it is difficult to see favor in any other information systems. In so many ways, it has never been simpler to get on a cloud platform. One credit card swipe, and you’re on your way. As a result of these conveniences and benefits, we live in a world where public clouds, private clouds, cloud services, and a litany of cloud technologies help improve business power and add to the flexibility of organizations.  

A Cloud Wake-Up Call

Despite all the advantages of cloud computing, many organizations under a cloud mandate today find their technology, operating, and migration costs are not what they anticipated. Many IT departments have legacy applications that are not cloud-ready, plus significant shadow IT and overly complex cloud infrastructures are creating severe cloud sprawl. Other organizations can't build technology around their business initiatives because they don’t have the staff, or the necessary efforts are beyond their capabilities. For these and other cloud situations, a cloud-first approach is making matters worse.

For these and other situations, the cloud-first strategy has uncovered a whole new world of challenges: 

• The need for cost and consumption control tools 
• Complex compliance mandates and technical requirements
• Cloud staff acquisition and retention
• Increased security needs
• Lack of visibility and metrics
• Bifurcation of IT infrastructures
• Underperformance of legacy applications 

A New Cloud Phase

Hybrid and multi-cloud technologies have proven their effectiveness in the enterprise as the best path forward by adding functional co-existence to the informational technology picture and unlocking the potential for an application-centric cloud strategy. Cloud-leaning philosophies hold untold business values, but not every application out there was built with cloud technologies in mind. There are far more traditionally structured applications than not. Some applications might be better suited to one cloud service over a given cloud-based solution. Factors like security, integrity, auditing, and performance might become more complicated or costly within the constructs of a limitless cloud environment. Depending on your organization’s capabilities, some applications are simpler to leave and integrate through on-premise systems.

Application First

In this second decade of cloud, a new phase is emerging in which business advantages must be achieved by focusing on the user and taking an “application first” strategy rather than a cloud-first strategy. While public cloud and private cloud architectures have extremely powerful benefits, there are too many challenges to navigate when enterprises insist on solely implementing one versus the other. As part of a strategic architectural practice, hybrid cloud and multi-cloud approaches can properly shift the focus to the users and the type of applications they interact with.  

Being smart about the cloud means using its benefits correctly, according to your business, your users, and your applications. We can even go so far as to say that if you have not developed an application and user-first strategy as of 2020, you will fall behind your competitors. Once you’ve built an intelligent strategy, it’s time to move on to a discussion of platform and infrastructure advantages, such as edge computing, artificial intelligence, machine learning, hybrid, auto scaling—technologies that are only possible with the more flexible application-first approach within hybrid, multi-cloud solutions, leaving cloud-first as an antiquated ideology.

Emil Sayegh is widely recognized as one of the industry’s cloud visionaries and "fathers of OpenStack" (he launched and led the cloud computing and hosting businesses for HP and Rackspace), can talk about everything from rising decentralization, to hybrid and edge clouds, and the significant impact that AI and Blockchain technologies will have on the industry. 

 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.

Artificial intelligence is a topic that we keep hearing now and then during discussions in different tech and business circles. Some people still associate it with science fiction movies, but the truth is, it is here with us and chances are, you might be interacting with it daily. The popular AI inventions include Siri and Alexa, both of which make our lives easy. These inventions have made AI a household name and Alexa is the evidence of its presence in our lives. While some people may see it as a new phenomenon, the concept is not new. It first came into existence as early as 1956, although it took decades to make progress towards making it a reality. Here are top AI and machine learning trends in 2021:

1. AI is aiding the fight against COVID-19

According to the World Health Organization, AI and big data are playing a critical role in the fight against the coronavirus pandemic. It has helped the healthcare providers to respond to the outbreak and manage infections in a variety of ways. For instance AI systems that use infrared technology to check temperatures of people in airports or bus stations are deployed in countries such as China. Similarly, thermal cameras are used to read temperatures before people could access public transport systems, or buildings to manage infections. Robots and drones on the other hand are helping doctors and other medical personnel in delivering medication and checking if social distancing regulations are followed in streets respectively. As the search for the best candidate for coronavirus vaccine and cure continues, machine learning will be at the core of research. 

• Transparency will be the main talking point

Although artificial intelligence has become ubiquitous, it suffers from trust issues. As businesses seek to increase the investments in this technology, they will first want to know that the technology is transparent enough and can be adopted with confidence. After all, no one wants to invest in technologies they do not understand. As such, there will be an increased push for transparency in AI projects and their deployments in 2021 going to the future. Although companies will try to understand the workings of AI models and algorithms, AI software developers and solution providers will be required to create solutions that are understandable to the users. 

• Automated ML

AutoML is slowly becoming famous as scientists seek ways of executing repetitive and tedious modeling tasks that once required weeks or even months of data personnel effort to complete. AutoML processes raw data that is input to it, chooses a model that makes sense, and finds patterns in the data inputs. It then finds the best model that should be applied to it. These are the activities that were once done by hand. An example is the Google AutoML that is a combination of recurrent neural network (RNN) and reinforcement learning. These systems are increasing the accuracy of ML systems and will be the main area of focus in the coming days. 

• IoT and AI are converging

IoT is becoming a critical technology in industrial processes. The advancements in AI algorithms are making it more useful and enhances efficiency. There is however a lot of room for improvement with the help of machine learning in areas such as predictive maintenance that allows users to understand machines and carry out maintenance of manufacturing equipment before it is too late. The power of predictive maintenance that is achieved as a collaboration between IoT and ML allows companies to use the technology to their advantage as a way of enhancing efficiency. An example of companies that have taken advantage of trends in IoT and ML is Rolls Royce that in partnership with Azure IoT Solutions is using it to check the health of their aircraft engines to increase uptime. Many companies are slowly adopting these technologies to keep their machines running as much as possible. 

Machine learning and artificial intelligence have been the leading topics of discussion in 2020. The coronavirus pandemic has made these technologies highly crucial than before, as they have shown the potential to help in many ways. Machine learning is now the driving force behind multi-billion industries such as medical diagnostics companies and autonomous vehicle companies, among others. With this immense potential, almost every industry is investing in it. Here are some trends that you should expect in 2021 going forward.

• Military autonomous systems

The military is one of the areas that have been monitoring the developments in machine learning and artificial intelligence more than anyone else. Machine learning has already been tried and tested by the military in drones and will soon control military ships and other operations. With machine learning, little human interference will be needed to man military systems as machine learning will start doing more. Going to 2021, AI will become the leading investment in the military, and we are likely to see more developments.

• Organizations will invest in machine learning

AI tools and platforms are making their way into businesses than ever before. As businesses look for ways to reduce the cost of operations, artificial intelligence, and machine learning have become highly critical in doing this. These tools allow organizations to find out better ways of serving customers and use data to find ways to adapt to new realities. These tools will continue gaining traction throughout 2021 as more organizations continue venturing into AI to enhance their effectiveness.

• AI and machine learning will be used increasingly for cybersecurity

Artificial intelligence and machine learning platforms and technologies are finding their way into cybersecurity both for corporate and personal use. As cyber-attacks continue becoming more sophisticated, developers are in a race to find technologies that would keep up with these threats. AI and machine learning technologies have shown the potential to be useful in fighting sophisticated cyberattacks such as DDoS attacks, malware, and ransomware, all of which are highly destructive. The future of AI-powered cybersecurity is becoming clearer by the day. These tools have shown the potential to gather data from an organization’s transactional systems, networks, websites, and other public sources and use algorithms to analyze potential threats and patterns- such as rogue IP addresses and potential data breaches. From 2021, AI and machine learning algorithms will be used increasingly in home and organizational security systems such as video cameras and intruder alarm systems.

• Facial recognition

Another area that has grown immensely over time is the AI-powered facial recognition technology powered by computer vision algorithms. As controversial as it is, this is an area that has shown immense potential to be deployed to identify individuals for security purposes. From now on, facial recognition technology will be deployed to help the police force in fighting crime. There will be more investment in AI-driven crime solutions, and surveillance will depend on these technologies.

• Ethical questions and concerns

There have already been various ethical concerns surrounding the use of AI-powered technologies, that include machine learning algorithms deployed in various areas. With time, these questions will increase and will become louder as people want to know what is in store for them concerning ethics. With these ethical concerns, there will be a need for laws to govern the use of IT systems such as facial recognition platforms by the police.  These laws will address the “deepfakes” misinformation, that has emerged to be a threat to society and cyberattacks, which have increasingly taken advantage of AI to increase their levels of sophistication.