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.

Artificial intelligence (AI) has evolved into one of the most advanced technologies that can be used in different industries for different things. A decade ago, when enthusiasts first talked about the potential of this technology, many laughed it off as just another fantasy and dreams that would never succeed. As we speak today, countries are trying their best to be the leaders in this area. It is the hottest buzzword in business circles and among governments as it is quickly proving to be a critical lynchpin of the digital transformation and automation that many players are seeking to take advantage of to reduce the cost of doing business and increase effectiveness.

Artificial intelligence and machine learning (ML) are now used in a wide range of solutions and applications. Their potential has seen them being implemented in manufacturing, governance, medicine, and content management to boost productivity and enhance effectiveness. With this trend, AI will be disrupting and reshaping industries significantly in the coming years.

What is artificial intelligence?

This is one of the questions that you are likely to ask yourself. The definition of AI has changed over time. However, the main idea is that AI is the building of machines that can think like humans. Humans are known to be unique in how they interpret things around them and use the information they gather to make decisions. The technology is built using humans as blueprints.

How does AI work?

At its basic level, AI systems use historical data to make future predictions. Through matching of data, these systems can generate an outcome that can be used to make decisions about specific topics of interest. AI is also able to simulate and learn the surroundings through sensors and give proper output.

Common Types of AI

Although there is confusion between AI, deep learning, and ML, ML, and Deep Learning are subsets of AI. The common types of machine learning that are in place include:

• Machine learning (ML)

Machine learning is the application of artificial intelligence, which gives systems the ability to learn and improve automatically without human input. ML aims at creating programs that can access data and use it to learn by themselves.

• Deep learning

Deep learning is actually the subset of ML, that is a subset of AI whose aim is to mimic the human neural network. It allows machines to learn by trying to mimic humans. The neural networks learn by processing labeled data, that is fed to them during training. This data is used to train systems and see the output produced after a given output. Once the systems have “learned” fully from supplied data and return an accurate result, it can be deployed to play an intended role.

• Natural language processing (NLP)

NLP is a branch of AI that enables computers to interpret, understand, and manipulate human’s natural language. It has various subsets, including natural language understanding (NLU) and natural language generation (NLG). While NLU entails the ability of machines to read and comprehend, NLG, on the other hand, is a branch that strives to enable the transformation of data into human words.

• Computer vision

Computer vision is a branch of AI that allows machines to identify and classify objects that they see. It also allows systems to react to what they have seen. Just like humans, computer vision allows machines to see and interpret objects around them and react accordingly. On top of it, they can measure things such as temperature and quality of air, some of that cannot be done by humans. With the advancement of deep learning, computer vision can learn many things, such as images, and translate them appropriately.

The coronavirus pandemic has put businesses, both small and large, under untold pressure never experienced before. Although the pandemic has ripped many industries apart, it has also come as a blessing in disguise. It enabled many companies to develop efficient, digital means of operating and leveraging on the power of AI and automation. Although there were many promises made about the potential of AI for more than 50 years, these promises are now being delivered finally with COVID-19 acting as a catalyst.

According to research, AI is estimated to contribute more than $15 trillion to the global economy by 2030. This will make it the biggest investment opportunity in the world. With the new realities of the coronavirus pandemic, there is a need to adopt AI more and make use of efficiencies and augmentations brought about by increased digitization.

As companies rebuild their workforce post-pandemic, they can avoid repeating the mistakes made in the past. They must start by ensuring that the workforce is diverse, beginning with the candidate pool. They can achieve this by enforcing a hiring process that does not select candidates based on their race or religion. Instead, recruiters and HR must, at all times, mitigate bias in the process of hiring. This can be achieved using artificial intelligence, which automates hiring and rejections. With the help of AI, a candidate pool is narrowed based on different decisions based on performance and qualifications, as opposed to race and religion, among other biases associated with humans.

As companies seek to emerge from the pandemic, artificial intelligence will help organizations adapt to the new norms. For instance, AI-enabled robots will be used to perform tasks that previously required human workers to do, such as recognizing objects and moving luggage in factories and facilities. This has already be seen during the treatment of coronavirus patients where robots worked side-by-side with medical professionals in delivering medicine and other equipment in hospitals. AI-enabled platforms such as augmented reality and virtual reality will help companies simulate work environments and train employees in real-world tasks while reducing travel costs to the real office space.

With the help of AI, companies’ costs, revenues, and operating models will be changed. AI offers companies a chance to build resilience in their manufacturing operations and supply chain. It also promises to minimize cost and damages, which hampered success in the past. It also allows businesses to operate in a larger number of small, efficient facilities closer to the customer. These are some of the lessons that the pandemic has taught us.

As the pandemic keeps biting across the world, consumer habits are also changing to match the new normal. This affects the revenues of many companies, that initially depended on the brick-and-mortar stores since companies have moved online. As focus shifts, companies are also deploying AI-enabled solutions to learn new customer behavior and address their needs. Thanks to the potential of AI in data analysis, companies will be able to identify changes occasioned by the coronavirus pandemic, identify new trends, and new consumer preferences.

Going forward, companies must focus their employees on tasks that add the most value, such as designing algorithms, strategically monitoring inputs and outputs, and redesigning processes to accommodate humans and their integration with artificial intelligence. AI is likely to be the leading game-changers in history, that will provide a wide range of opportunities, such as efficient service delivery and greater on-demand services. In the post-COVID-19 period, successes of organizations will be determined by the way they adopt new measures and manage the tension that might be brought about by other pandemics in the future.

What was once just a figment of fiction and stories in movies, artificial intelligence (AI), has now made way in our everyday lives. AI is evolving — literally from what it once was to something much greater than what many anticipated. The evolution is made possible by a software that borrows concepts from the Darwinian theory of evolution whose basis is on survival for the fittest. This software improves the software without any human input. This is a result of years of hard work and research in AI, which scientists believe could lead to the discovery of higher quality AI in the future.

While building AI takes time, the use of neural networks, a type of machine learning, allows machines to mimic the brain structure of humans. These networks learn from training data by changing connections between artificial neurons. Unlike a few years ago, where scientists had to work seamlessly on all stages to ensure the systems learn, scientists have automated some steps. However, the current methods still rely on humans in some aspects.

AutoML-Zero, a program developed by Quoc Le and other scientists at Google, is an example of an AI program that requires zero human input to learn. According to Quoc Le, the goal of the program is to allow machines to learn by themselves and improve. AutoML-Zero discovers algorithms by approximating evolution. The program begins with creating about a hundred candidate algorithms through a random combination of mathematical operations. The next step entails testing them on a simple task such as an image recognition problem. The algorithms will be required to decide what is on the picture, a cat, or a truck. While the algorithms that exhibited better performance are kept, those that have decreased performance are killed off. This is just like nature.

The performance of each algorithm is measured in each circle against the algorithms designed by hand. Those that show better performance are retained while the ones that exhibit weakness are left. The cycle repeats as it advances to the next stage. It mimics the theory of evolution fronted by Charles Darwin, where the strong survive and is moved to the next phase. For the OpenML, for instance, thousands of populations are created at once. This allows tens of thousands of algorithms to be assessed in a second until the right solution is found. Further, tricks such as exchanging algorithms between different populations are used to speed up searching. These tricks are also useful in preventing evolutionary dead ends and also removing duplicate algorithms. According to engineers at Google, neuroevolution can cultivate an algorithm that is 94.6% accurate, based on the four repeats of the experiment that were conducted. The mutations have improved the image recognition capabilities of the algorithms.

The evolution of AI will have a massive positive impact on solving real-world problems. There is so much potential for artificial intelligence, which, if tapped, will influence the world significantly in the future. For instance, thanks to AI, there has been a substantial increase in workplace productivity. By 2030, AI will become common in everyday life in areas such as self-driving cars, weather prediction, research, and space exploration, among others. With the machine learning algorithms, incidences such as cyberterrorism and fraud will be prevented effectively. This will be useful in the current age where there is an increasing public debate on the rising cases of fraud and privacy concerns online. Accordingly, AI will significantly impact healthcare due to its potential to analyze large amounts of data, leading to accuracy in the prevention and treatment of different medical conditions and personalization of care.

While it is early to predict everything that the evolution of AI will do to different industries, the progress is pleasing.

Artificial intelligence could be vulnerable to hackers, reports ZD Net.

Cyber criminals could exploit emerging technologies including artificial intelligence and machine learning to help conduct attacks against autonomous cars, drones and Internet of Things-connected vehicles, according to a report from the United Nations, Europol and cybersecurity company Trend Micro.

Read the article on ZD Net

New York City is limiting how much artificial intelligence is used when screening job applicants. 

New York City is trying to rein in the use of algorithms used to screen job applicants.

Read the article on CBS New York

According to Sci Tech Daily, researchers are working on teaching neural networks how to evaluate the accuracy of their decisions.

Increasingly, artificial intelligence systems known as deep learning neural networks are used to inform decisions vital to human health and safety, such as in autonomous driving or medical diagnosis. 

Read the article on Sci Tech Daily

Research from the University of Saskatchewan may make artificial intelligence capable apps run faster and more efficiently on your devices, reports Tech Explorer.

Tired of Siri or Google Assistant draining your phone battery? A new University of Saskatchewan (USask) artificial intelligence computer model holds promise for making "smart" apps such as Amazon, Apple, and Google's virtual assistants safer, faster and more energy efficient.

Read the article on Tech Explore

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 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.

Machine Learning and Artificial Intelligence (ML and AI) have become some of the most talked about technologies in the recent years. This has been occasioned by the prominence that these technologies have gained and the advancements in this area. As such, big tech companies such as Google, Microsoft Azure and Amazon are the leaders in this area, having developed different ML platforms. While AI and ML may appear to be alien terminologies to many, we have been experiencing it without us even being aware of it. Some of the areas that you might have come into close contact with this technology are in image tagging by Facebook as well as spam detection by email providers. For Facebook, for example, uploaded images are tagged automatically using face or image recognition algorithms while email providers such as Gmail use ML to recognize patterns or selected words then filter spam messages accordingly. Now let us look at some problems that arise from machine learning.

• Understanding processes that should be automated

Before deciding to use machine learning algorithms, one has to understand problems that exist and evaluate the ones that should be solved using this technology from the others. Start by finding the processes that are done manually with no variable output and are repetitive in nature. Complicated processes need to be inspected before automating them. Although ML can help in the automation of some processes in your business, not every process can work well if automated. This is what many people fail to understand before embarking on the automation process.

• Poor quality data

As effective as machine learning can be, lack of quality data is making things harder. Machine learning algorithms require properly sorted data which is essential for quick decision-making. Some of the issues facing data currently are dirt, incompleteness, and noise. All these aspects are the true enemies of the ideal environment for machine learning. The solution to this problem is to evaluate and scope data by enforcing the right data governance policies.

• Inadequacy in infrastructure

Machine learning requires extensive infrastructure to accommodate the vast amounts of data that originate from various sources. Traditional systems have no capacity to handle the massive amounts of data that originate from diverse sources such as social media, call-centers and websites. Some organizations often fail to understand the capabilities of the available infrastructure before embarking on machine learning initiatives. You must understand your infrastructure and their potential to handle machine learning.

• Irrelevant or unwanted features

Machine learning depends on training data for it to be successful. However, if the training data contains irrelevant features or is not accurate enough, the results that were expected will not be given. For machine learning projects to be successful, good features must be selected to train the model.

• Inaccessible data and data security

Availability of data is one of the leading machine learning challenges that is faced by businesses. Raw data availability is important for companies as it gives them data to train their algorithms. For proper training to be conducted, huge chunks of training data are required. Little amount of training data or items will not be sufficient to train models and correctly implement machine learning. While gathering data is one of the problems, it is not the only one. Data are needed to model and process data to suit the algorithms that you need. Secondly, data security is also another issue in machine learning. Once you have the right data, security must be ensured to guard information against unwanted access. This begins with differentiating sensitive data from insensitive ones and understanding how machine learning can be implemented appropriately.

Imagine you walk into your dispatch office and see that your vehicle has broken down, your driver is running out of available hours, and inclement weather is delaying your next job. As the fleet manager, you now have to decide how best to handle this perilous situation. You have relied on disparate data sources to get ahead of the problem, but still you struggle for insights that can drive good decisions. Enter: Telematics 2.0 – the evolution of telematics.

To solve these issues for fleet and mobile asset managers, telematics will evolve from being a “dot on a map” tracking solution to a digital automation platform that ultimately guides an effective and fast decision-making process. The secret ingredient is artificial intelligence (AI), the programs that process all your data and give you exactly what you need to know in real time. In its core, there are four steps to get there.

1. Awareness: Data comes in many shapes, sizes, sources, and colors. We see the fundamental first step in telematics evolution as “Awareness,” central data aggregation repository that can collect data from everywhere. Not just GPS tracking on a vehicle, but cameras, people, infrastructure, other vehicles and much more. With IoT sensors becoming ubiquitous and APIs becoming standardized, we are fast connecting the physical world to digital awareness.

2. Intelligence: What do you do with all this data and awareness? A next step that we had serendipitously fallen into was reporting, alerting, and broadly classified as business Intelligence. I take this as a “trying to report on the data,” sometimes uncovering things you didn’t know (intelligence) and mostly reinforcing your gut instinct with visible data (bias validation). This solved some problems but didn’t get to the real “why?” which takes us to the next phase – cognition.

3.  Cognition: To answer “why?” something happens, you need a deeper contextual understanding of the situation. For this, we look beyond the digital signals from a source, and start looking for the intent of an action, layering in external factors, measuring the outcome in different situations, and creating a holistic view of why things happen. This is critical in going from repetitive robotic processes to real automation, where decisions can be made and enacted per the capability, situation, surrounding and time.

4. Automation: Automation will transform work. It starts with augmenting your everyday tasks. Today, you, “the operator,” are at the end point of awareness and intelligence and make the final call on every decision. In this phase, we leverage cognitive insights to automate real time operational decisions and provide deeper insights that may lead to business automation.

AI at Work

Each of these phases provide immediate and immense value and evolve simultaneously. The ability to connect, synthesize, analyze data at scale is AI at work. Not only does it automate our most mundane and repetitive processes but the instant insights from AI-powered telematics support decisions that save money and, possibly, lives.

By adopting state-of-the-art programs that can track multiple data sets on all of a fleet’s vehicles, AI-powered telematics platforms can predict what will happen next and empower fleet managers to make the best decisions with regard to pricing, scheduling, routing and driver accountability. Some of the functions that enable increased safety include:

• Spotting mechanical problems before they arise
• Recommending secure routes
• Accurate scheduling keeps drivers from being overextended
• Dash cams can detect diver fatigue

Additionally, the use of highly evolved telematics holds the potential to increase operational efficiency and decrease institutional waste. Between surging insurance prices and rising compliance costs, fleet managers are being squeezed on multiple fronts. With little room to maneuver and grind out a slim margin, many have turned to telematics to ensure operations run as smooth as possible.

In addition to increased safety, the economic benefits of a data-driven fleet have been the silver bullet for many fleet managers, keeping their businesses successful and growing even during the economic slowdown. Some of the AI-fueled efficiencies are as follows:

• Smart routing and auto-calculated pricing that’s quoted according to likely future resources and logistics, such as traffic patterns and driver/asset availability
• Predicted fuel usage based multiple data sets, including driver behaviors, altitudes and vehicle condition
• Holding drivers accountable by comparing their delivery times to other metrics and watching behaviors in real time through AI cameras inside the cabins

In conclusion, cloud-based telematic platforms that are built on an A.I. foundation hold the key to our transportation future. There is an immediate need for this technology in the fleet management space, and by adding data sets and tweaking algorithms, fleets can only get smarter, safer and more efficient over time.

This is a culmination of a technological evolution that will guide our fleet management decisions today and many years from now.

Sid joined Teletrac Navman as Senior Director in October 2014. Sid leads product management, strategy and UX at Teletrac Navman. Sid has more than 15 years of experience in various transportation sectors like precision agriculture, aerospace, connected cars, and fleet management. Prior to his current position, Sid held product, strategy, design, and engineering roles with industrial leaders such as Trimble, Deere, Honeywell Aerospace and Magellan. Sid holds a BS in Electronics Engineering from Mumbai University, India and an MS in EE-Avionics from Ohio University.

 Bias is one of the biggest documented weaknesses of humans. This can be seen from implicit tests that show that we are not even aware of some of our biases. Although we may say that these biases are part of human nature, experiments have shown that they can affect our judgments and outcomes. Over the past few years, there has been an argument on whether human biases can be solved by artificial intelligence or whether these biases can make their way into AI systems and cause harm. This comes at a time when AI is being deployed by many companies across different industries. The industries need to be aware of the risks of biases ad how AI can be affected by it.

All AI models are developed by human beings and can, without a doubt, have some form of bias. For instance, machine learning algorithms have biases that are a reflection of organizational teams, designer teams, and data scientists. They are also vulnerable to the prejudices of data engineers who gather data. Just like the lack of fairness that we have been experiencing from human decision-makers in different areas, AI is expected to also suffer from it as these prejudices can find their way into algorithms in a variety of ways.

AI systems learn to make decisions based on training data that can be as a result of human prejudice. As such, decisions that these systems can arrive at may sometimes be biased. They can be seen in areas such as gender, race, and sexual orientation, among others. Amazon, for instance, stopped using its hiring algorithms after it emerged that it favored applicants based on specific words such as “execute” or “captured” that were common in men’s resumes. Another major flaw in AI systems that results in bias is data sampling whereby groups are over or underrepresented in the training data. This results in specific groups, such as minorities and women suffering.

Despite the possibility of bias, AI will have some level of trustworthiness, which is a backbone of machine learning. However, even a trustworthy model will have some form of weakness that must be looked into if fair systems are to ve developed. It is upon humans to ensure that training data is fair and representative by all standards. To achieve this and ensure optimal results, organizations must have tech teams with diverse members. These members should be in charge of building AI models and creating training data to be used by AI systems.  Furthermore, organizations should try to find out a highly comprehensive data and experiment it with different metrics and datasets.

As machine learning and artificial intelligence systems increasingly become complex, it has become critical now than ever to have training data that is annotated by humans in an unbiased manner. During the training of machine learning models, human bias can damage the accuracy of these models. If there is are in-house teams that are involved in interpreting training data, they should adhere to an unbiased approach in the classification of data. This may involve using different approaches and styles that are diverse.

Without a diverse approach in classifying data, there is a high risk of less accurate models being created. On the other hand, the external partners who are involved in the collection or processing of data should ensure the job is done by diverse crowds to ensure data is representative. The data annotation design tasks should be carried out carefully. Once the training data has been created, it should be checked to identify whether data has any bias or not.