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, mostly known as AI, has been there for decades. Although it was mainly associated with robots, AI has today become part of almost every industry. From mobile phones and other personal gadgets to home appliances, AI has taken over almost every sector. As the number of sectors embracing AI keeps rising, new AI trends are emerging in the market. Here are some trends that you should expect in 2020 going forward in artificial intelligence.

• There will be a proliferation of digital workers

The use of digital workers across the globe has been growing significantly. In research by IDC, the use of digital workers such as software robots and AI will grow by over 50% by 2022. Going forward, many digital robots will take up some office tasks. These AI-enabled workers will be trained to perform business tasks that were strictly done by human employees, only that they will do it faster and with fewer mistakes. Expect employees across organizations to work with digital workers side by side.

• More devices will be AI-powered

As the hardware, software and expertise needed for AI become cheaper and available, many devices will use this platform. As this decade begins, the cost of hardware and software continues to drop, and AI will be increasingly embedded in devices such as phones, home appliances and other gadgets. Vehicles and workplace tools will also be embedded with artificial intelligence. AI will be augmented by technology such as augmented and virtual reality displays and other modern technologies such as the Internet of Things that will then enable more devices to learn and think by themselves.

• There will be an increase in process intelligence

If you do not know it already, process intelligence enables organizations to use information that is in their systems to create a visual model of processes and analyze them in real-time to find out bottlenecks and predict the future for improved decision-making. As the corporate world become more competitive by the time, companies will increasingly deploy AI technologies to take advantage of accuracy and speed that this technology promises. This technology will become a standard for many businesses from now going forward.

• Human and AI cooperation will increase

As more and more industries continue embracing artificial intelligence, humans will get used to working alongside these technologies in their daily operations. Bots and other AI-powered tools will become part of everyday life. With these tools in place, humans will be required to learn new skills or new ways of working alongside AI-enabled technologies. According to IDC, 75% of organizations will be investing in retraining employees to fill the gaps that are created by the need to adopt AI by 2025. This trend will become clear in 2020 to the point that organizations will begin investing in AI tools and training.

• AI will be used more than ever in cybersecurity

As cybersecurity incidences such as hacking, malware attacks and others keep rising in number and sophistication, AI algorithms will become increasingly useful in averting these attacks. These attacks are themselves powered by AI’s sophisticated algorithms, and smart technology will become critical in protecting systems from potential intrusions. AI will be used in the future to detect signs of malicious activities and transactions by following patterns that indicate their suspicious behavior and raise the alarm before the systems are breached and data stolen. With the rollout of 5G now taking shape, it is expected that more attacks will be experienced and severity, as well as sophistication of attacks, will rise more than before. The only way that these attacks can be fought is by deploying sophisticated AI algorithms to detect and counter them.

Generally, AI has the potential to reshape how we go by in our daily operations in places of work and personal activities. With the increasing trend towards AI, we expect to see more and more AI uses and solutions in workplaces and around us.

The fear of artificial intelligence and robotics when it comes to jobs being lost has been with us for decades. However, it is also emerging that beauty contestants and Instagram influencers may face stiff competition from automation soon, too.

On Instagram, Miquela (@liliquela) is a perfect example of the future potential of artificial intelligence. Miquela is an AI-based influencer with more than 2.6 million followers. Just like any other fashion influencer, she perfectly plans her posts and shares her insights in matters fashion regularly. While everything might be similar or even better than those of humans, the main difference is that she is fake.  Unlike most influencers, Miquela is a virtual influencer characterized by beauty, fantastic personality, and millions of followers, most of whom many human influencers cannot accumulate over a lifetime. She is also mysterious, yet computers made her look like a warm and charming human being without people noticing.

In January last year, TechCrunch reported that the creators of Miquela had obtained a $125 million investment from various players led by Spark Capital. These investors saw virtual influencers as the future of ads in fashion and commerce as well as other sectors. Miquela proved this through her collaboration with Prada for Milan Fashion Week. In this collaboration, 3D-generated gifs of Miquela wearing summer and spring 2018 collections were created and posted on her Instagram account. Virtual people might be artificial, but one thing that is for sure is that people will continue embracing them in different spheres of life. With this development, precedence has been set, and we are likely to see more of this in the future.

Other brands are following in the steps of Prada in embracing the virtualization of advertisement. Last year, Balmain announced that it was deploying the “Balmain Army” with CGI models to showcase its latest designs from its BBox line. She has also been featured in Hypebeast and V Magazine wearing luxury brands such as Balenciaga and Kenzo, which is more than a human model or influencer could ever wish for.

Apart from Miquela, other Instagram influencers are taking over social media by storm. One of them is Laila Blue, that came into existence in 2018 in Dubai. Laila is half-French and half-Lebanese. Others are Noonoouri, Imma, and Bermuda. All these virtual influencers are focusing on the modeling industry, that seems to attract most of them now.

The development means that high-resolution virtual people are coming for the jobs initially reserved for human influencers for the last decade or so. However, what no one fully understands is whether they will take over the jobs that are known to belong to models. While there is no evidence now that this could be the case, it is not clear whether virtual people will make their way into other areas apart from Instagram and social media.

So what is next for Miquela and other similar virtual influencers? It is already clear that the fashion industry and social media love her. With competition among the innovators to extend the use of virtual people to other industries, I believe that we are likely to see more advancement in technology in the future. In the fashion industry, robots and artificial intelligence-based technologies will likely take over some marketing initiatives. Similarly, social media marketing, which has been solely done by influencers, will be shared between the human and virtual influencers.

With virtual people now a reality, this novelty technology is destined for a great future, just like blockchain technology. It could take over all the hot people’s jobs, some of which are a source of income for many people.

AI has been hailed as one of the top technologies that will revolutionize the world and change things for the better. However, it is not without some concerns and drawbacks. It is for this reason that many companies and governments are investing billions of dollars in artificial intelligence projects. Due to investments, AI now powers many real-world applications, ranging from robots to facial recognition systems and assistants such as Siri and Alexa. In fact, we barely notice our interaction with AI while using some common real-world applications today.

With the AI’s proven potential, companies across different sectors are increasingly implementing it in their operations. This technology is promising considerable benefits through the enhancement of productivity and improvement of innovation. AI also promises to impact work for the better as some occupations are expected to emerge while others are expected to become extinct as AI takes over many operations. As demand for some skills increases, there will be reorganization in many industries to fit into the new order of things.

As it stands, healthcare is one of the areas that are likely to benefit substantially from AI. An example of advances in this industry is DeepMind, that can predict Acute Kidney Disease early enough. Since this disease is life-threatening, early prediction can help save a life through early detection and treatment. Other applications of AI in healthcare include detection of cancer through image recognition and drug research, where AI, in collaboration with deep learning, has shown immense potential. Operating rooms also seem to be the next destination for AI. This technology has shown the potential of operating patients with near-perfect accuracy compared to human surgeons.

AI can help in designing better buildings with optimal conditions. This is if the Spacemaker AI is anything to go by. With such technologies, better buildings with optimized lighting, wind, and adaptation will be assured. Although humans have for long led in calculations and architectural designs of buildings, seeking help from AI will lead to the construction of energy-efficient houses with low operational costs. Management of the same building and machines used in different industries can also be led by AI, that will predict when there will be a potential breakdown so that early repairs can be made.

In transportation, AI can be used to plan routes and find solutions for the smooth flow of traffic. With the help of simulations, queues can be reduced through optimization, that is made easy by integrated AI solutions. These capabilities can be applied in areas such as logistics, where AI can assist drivers in driving more smoothly and avoiding traffic.

While AI has many positive uses, there are some areas where its adoption will not be so good. We cannot start this without mentioning the implications of AI on privacy. As hacking incidences keep increasing in the world today, one of the concerns of many people is the implications of AI on the privacy of users. Things become more complicated when one thinks of the potential of this technology and how data and personal information can be used in artificial intelligence and machine learning scenarios. As of now, the opaque nature of AI and its associated technologies means that most people, even seasoned engineers, may not be fully aware of how AI systems are using data that fed into them. In the age of deep fakes, AI will likely be used maliciously by bad actors for criminal purposes.

The implication of AI in employment is another big issue that many people are concerned with now. AI is likely to replace humans in many areas of employment as organizations adopt this technology to enhance efficiency and reduce operational costs.

While AI has some negatives, it is here with us, and it is about time we accept change and move forward to accommodate the technology and the good things that come with it. Considering the adverse impacts of AI are fewer than the positives, potential problems should be studied and addressed in time.

According to Tech Explore, Facebook has developed a new AI benchmarking program that includes humans interacting with the AI. 

Advances in artificial intelligence depend on continual testing of massive amounts of data. This benchmark testing allows researchers to determine how "intelligent" AI is, spot weaknesses and then develop stronger, smarter models. 

Read the article on Tech Explore

Artificial intelligence can be used to predict loneliness in older adults, reports Medical Express. 

For the past couple of decades, there has been a loneliness pandemic, marked by rising rates of suicides and opioid use, lost productivity, increased health care costs and rising mortality. The COVID-19 pandemic, with its associated social distancing and lockdowns, have only made things worse, say experts. 

Read the article on Medical Express

Artificial intelligence is training on text and images and may soon be able to caption pictures correctly, reports on MIT Technology Review. 

Of all the AI models in the world, OpenAI’s GPT-3 has most captured the public’s imagination. It can spew poems, short stories, and songs with little prompting, and has been demonstrated to fool people into thinking its outputs were written by a human. But its eloquence is more of a parlor trick, not to be confused with real intelligence.

Read the article on MIT Technology Review

Organizations need to convince the public that artificial intelligence is safe, reports Information Week. 

Security and privacy concerns are the top barriers to adoption of artificial intelligence, and for good reason. Both benign and malicious actors can threaten the performance, fairness, security and privacy of AI models and data.

Read the article on Information Week

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. 

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.

Minecraft is a popular sandbox multiplayer online building game. It starts with a generated world where terrain and features are created with building blocks. There are also generated characters called “mobs” that appear throughout the world. The two types of gameplay in Minecraft are survival mode and creative mode. In survival mode the player can be harmed and has limited resources. In creative mode the player does not have to worry about being harmed and has access to all resources. In creative mode the player’s imagination is the limit and there have been endless creative projects that have been carried out in the game including recreations of cities and landmarks among others. 

CraftAssist is a framework created in 2019 that features dialogue-enabled interactive assistants. These assistants collaborate with human players in the game. The assistants can build, move objects, spawn mobs and communicate with players by chat. The framework is intended to research how agents interact with players and discover the possibilities between them. The framework is open source to encourage AI researchers to use the toolset. 

Players can interact with other players and the AI assistants, just like in a regular game of Minecraft. CraftAssist includes a baseline assistant, who brings over intelligence of previous interactions. There is also a house dataset that was put together using crowd sourced participants. These participants were tasked with constructing a house within 30 minutes and their actions were recorded. Both of these datasets are part of the core CraftAssist release.

Minecraft is played through a client/server architecture and the bot communicates through the Minecraft protocol. The Minecraft protocol does not require server side mods, which allows the bot to join in with other players and bots. The bot assistant is modular and this allows research to be conducted based on the individual models. These include modules related to perception and memory among others. The bot modules work together to perform tasks and interact with the human players. A human player may request that a bot build part of a house such as the roof. The bot then takes the chat commands and performs high-level actions that carry out the informal chat instructions to build the roof. 

Another example would be a player telling the AI assistant to build a house in the shape of a rectangle near the water. The modules take the given instructions from chat and create a program for the assistant. In this example the object water would be identified as being close and the assistant would be given coordinates to move to and start building. Machine learning works well for difficult and well defined tasks. One of the larger goals with CraftAssist is a framework that takes the often ambiguous human language commands and creates executable actions through the assistant. The interaction between humans and bots is a core focus of CraftAssist. 

More data from past interactions is being added to the assistant for further improvement. If you are a Minecraft player I would recommend giving CraftAssist a try to help build and improve the CraftAssist project!

CraftAssist Documentation:

https://research.fb.com/wp-content/uploads/2019/07/CraftAssist-A-Framework-for-Dialogue-enabled-Interactive-Agents-v3.pdf?

https://github.com/facebookresearch/craftassist

For decades, artificial intelligence has been seeking to mimic humans and nature. The main goal here is to create systems that have the ability to make decisions like humans. As such, most AI systems are brain-inspired, and scientists behind it have always attempted to mimic neural networks, a term borrowed from biology. With computers now able to think like humans, computer scientists are now focused on a different goal as they seek to revise artificial intelligence by transitioning it into smarter and more efficient systems. Initially, most of the research was devoted to training AI through machine learning to enable algorithms to learn how to recognize objects and how to perform basic tasks. Now that this has been achieved, these techniques are being deployed to create more advanced systems that can work autonomously without human intervention.

Google has been working on AutoML (automatic machine learning system), a smart machine learning system that can be mutated into a next-generation AI. This new system has been enhanced through the addition of the Darwinian evolution concept that allows building of AI products that can improve themselves. The product of this improvement is called AutoML-Zero. While this system can be worrying to many, if used well, it can open doors for the development of much smarter systems that can mimic human brains in development and decision making through enhanced evolution.

Going back to Charles Darwin’s concept of evolution, slight or random changes in the genetic makeup in an organism are either advantageous or disadvantageous. If the mutation allows the organism to survive and reproduce, the characteristics are then inherited by the future generation. Otherwise, the mutation dies with the organism. In AI, this phenomenon is known as neuro-evolution. It is a process in that neural networks recreates that built parts of the brain to allow only the strong or smart to survive.

An AutoML system makes it easier for applications to use machine learning and other automated features, unlike AutoML-Zero, that requires human input. It uses a three-step process that is setup, predict and learn to improve itself. The system begins with a selection of 100 algorithms by analysis of their mathematical operations. It then identifies the best of these algorithms through a trial-and-error approach. The best ones are retained and tweaked for the next round of trial-and-error while those found to be less effective are dropped. This is basically a mutation of neural networks. The process goes on and on until the neural network is highly efficient.

The new code is more effective and can efficiently identify things that have always been hard for AI to differentiate, such as the difference between objects. These best performing algorithms are kept in a similar manner as survival for the fittest fronted by Charles Darwin. According to researchers at Google, this Darwinian concept of neuroevolution can lead to the creation of an algorithm with 94% accuracy. Various studies saw an improvement in the algorithm’s image recognition skills.

The evolution of the machine learning algorithms, according to researchers, has led to the creation of more artificial intelligence systems that can work more like humans and without bias, that humans are known for. The mutation of algorithms will see the emergence of systems that can perform several tasks, including complex tasks that have for long relied on human expertise.

Despite the complex nature of the development of AI systems and advanced research that is needed to deliver highly efficient systems, Google’s AI mutation approach has shown to be a giant step towards making cutting-edge AI systems that can be helpful in the future. It is through such initiatives that we are likely to see systems that can outsmart humans in every aspect sooner rather than later.