Artificial Intelligence (AI) has been hailed as a revolutionary technology that will take the world to new heights. Experts claim that AI will revolutionize almost every industry and make human work easier, efficient, and less costly at the same time. Unlike the talks a decade ago where AI was just science fiction in Hollywood movies, this decade is becoming an era characterized by AI-driven automation and autonomous systems such as cars and other systems. With all these advantages, this technology is not without its drawbacks.

The risk of artificially intelligent systems taking over most human tasks might be a danger that can lead to things we did not anticipate if not carefully looked into. The delegation of duties and entrusting of complex decision processes to the evolving AI systems is already happening. They range from diagnosing diseases, admitting students to colleges, job interviews, and managing interactions with the clients through bots. We see an increasing focus of automakers on autonomous vehicles and the rise of autonomous weapons and even autonomous planes. While all these are developments worth embracing, we must also think about their downsides and how they can harm humans. The main concerns associated with AI systems are in different forms ranging from accuracy, privacy concerns, discrimination, transparency, trust, and security-related issues.

As we adopt AI, it would be a big mistake to think that machines will make equitable and fair decisions. Since the algorithms are made by humans and bias is likely to be built in, AI can be automatically biased just like humans. Some professionals have already discovered biases against women and people of color in some AI algorithms. According to Olga Russakovsky, a computer science professor at Princeton, AI biases go beyond gender and race. They go as far as creating socioeconomic inequalities and biases on the basis of education and work as well as social mobility. AI also has the potential to circulate false data and biased opinions, that can poison debates. With dependence on big data and machine learning models, AI can end up recycling historical biases.

Security is also a major AI risk. With the increased adoption of AI, security risks have not been addressed adequately. They have not been covered in the AI regulation debates as they should. This makes security one of the potential troubles in the future. Any AI system- be it a robot, networked systems, or programs- carries security risks that must be understood and addressed at the right time. The security risks stem from the design and development stage. Now that AI is expected to be autonomous in decision-making, humans are likely to lose control, and design flaws are likely to cause problems.

In addition to security, there is also concern about privacy and how AI will adversely affect it. AI-based facial recognition systems have been the major talking point in this regard. An example is in China, where these systems are used to monitor people in public spaces, buildings, and schools. Authoritarian regimes can use such data and systems to crack down on dissidents and make arrests. For western democracies, it may roll back progress that has been made with regard to human rights and freedoms.

AI has also led to the rise of fake social media “personalities,” making it difficult to differentiate real people from fake ones while also hastening fake audio and video “deepfakes” created by manipulating voices and images to resemble those of real people.

With AI adoption moving at an unstoppable pace, perhaps it is about time we become smarter and address these troubling concerns before they become too deep in our systems.

Artificial intelligence (AI) is a major talking point in almost every industry in the world today. Many professionals have identified it as the main driver of innovations in robotics, big data, and the internet of things (IoT). As a critical component in the era of automation, connected mobility, and innovation in agriculture, logistics, and aerospace, to mention but a few, the global AI market is expected to reach $190 billion by 2025.  As such, AI is a technology that must be on everyone’s radar in 2021 as it will continue impacting different industries in the foreseeable future. Here are some trends in ethical AI that will shape 2021.

• AI will progress towards trusted data

According to predictions by Forrester Research, 2021 will show the real impact of AI and the dark side of artificial data emanating from organizations. The data comes in two forms, synthetic data that allows users to develop training data sets to train AI and fake data. Companies are having a hard time with consumers and other interest groups and regulators to provide the right data and lineage for AI. This includes showing the audit trails for compliance purposes and ethical use of AI. This year, blockchain and AI will work together seriously than ever to enhance integrity, support provenance, and improve data use.

• More demand for ethical AI

There have always been talks about AI and ethics. Although AI is a promising field, there is concern about privacy, fairness, security, and AI confidentiality. Companies such as Google have come up with policies that seek to entrench ethics. In 2021, the demand for ethical AI will be at the top of the AI trends list. According to Forrester Research, CIOs will have a duty to respond to digital acceleration and manage uncertainty in AI use.  As AI and the ethical part of it increasingly become an area of concern, you are likely to see some companies deliberately choosing to do business with partners who commit to data ethics and ethical data handling practices that reflect their own values and customer desires.

• The enhanced role of AI in healthcare

The coronavirus pandemic came as a surprise, and it caught the healthcare industry unaware. Thanks to technology, healthcare handled the crisis reasonably well using concepts of big data and artificial intelligence. AI was used to identify COVID-19 patients through thermal cameras and other mobile applications, and critical areas of the pandemic. This technology is already helping the sector increase accuracy, and researchers are using machine learning concepts to search for an effective treatment for the disease. By leveraging AI and data analysis, professionals will be able to predict various outcomes to help healthcare service providers. AI and ML instruments will be widely adopted in healthcare as the pandemic persists to help doctors track patients' health remotely.

• AI will boost automation and augmentation in workplaces

  The coronavirus pandemic has brought the biggest disruption in the workplace than anything else. In 2021, more companies will look for help from artificial intelligence to manage workplace disruption. This involves applying AI for intelligent extraction of documents, studying human resource needs, tracking work and performance, and using autonomous robots to speed up processes and reduce the cost of operations. According to Forrester, more than 60% of data and analytics decision-makers in companies are adopting AI in their decision-making initiatives.

Generally, organizations will look for ways to apply AI safely and ethically to emerge stronger both in the short and long term. Although it might have helped in various aspects during the pandemic, executives will benefit from AI in rethinking their future considering its efficiency, flexibility, and reliability.

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

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

Diverse Teams

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

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

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

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

Establish Governance

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

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

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

Educate Employees

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

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

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

Consistently Review

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

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

BT Global Head of AI & Research Dr. Detlef Nauck

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

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

• Demand for AI talent will continue to be high

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

• There will be a competition for AI-enabled chips

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

• Augmented processes will emerge

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

• Growth in AI applications

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

• AI ethics and standards will be a leading focus

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

According to Tech Republic, the focus should be on machine learning not artificial intelligence. 

"People are getting confused about the meaning of AI in discussions of technology trends—that there is some kind of intelligent thought in computers that is responsible for the progress and which is competing with humans. 

Read the article on Tech Republic

Artificial intelligence is being used to create “new” songs by artists like Amy Winehouse and Nirvana, reports Dazed Digital.

Cyber popstar and open source vocaloid Hatsune Miku shifted the parameters for music as we know it. Since Miku, there’s also been the emergence of AI-powered musicians, like singer-songwriter Yona. Now, a new AI project is generating so-called lost hits of the infamous ‘27 club’, from Amy Winehouse to Nirvana.

Read the article on Dazed Digital

Machine learning could decipher the “biological language” of cancer, reports Medical Express. 

Powerful algorithms used by Netflix, Amazon and Facebook can 'predict' the biological language of cancer and neurodegenerative diseases like Alzheimer's, scientists have found. 

Read the article on Medical Express

According to C4ISRNET, the Pentagon is seeking help to get data ready for use with AI. 

The Pentagon’s Joint Artificial Intelligence Center is recruiting businesses to help prepare military data for use with AI.

Read the article on C4ISRNET

Digital transformation is one of the most important drivers of how businesses deliver value to their customers in a competitive, fast-changing business environment. The process aims to leverage digital technologies to create or modify user experiences and business processes, thus meeting users’ changing needs and the market.

Artificial Intelligence (AI) is now recognized as one of the main enablers of digital transformation in multiple industries. AI can help companies become more innovative, flexible and adaptive than ever before. Many think of it as a future or visionary technology, but the opposite is true. AI is already being cost-effectively deployed in numerous companies, accelerating productivity and competitiveness, while helping speed digital transformation.

What is Digital Transformation?

Digital transformation is a set of processes, tools and methodologies used by businesses to optimize their operational activities, such as offering differentiated service, increasing performance and expanding their reach. More than a concept, digital transformation is a dynamic movement; it attracts organizations looking to review processes, innovate and gain competitiveness with the help of technology. Artificial intelligence is a critical strategic factor for businesses to expand their impact.

AI and Digital Transformation

Digital transformation took a big step forward when AI and machine learning became part of business strategy. Aside from boosting productivity, these technologies are essential because they enable better use of the data collected by a company. With useful data, businesses can expand, improve their current products and services, and create innovative strategies.

Here are three examples of how digital transformation coupled with AI can deliver top results:

1. 360-Degree View of Customers

AI helps businesses, especially their marketing sector, understand customer needs and preferences. Every digital transformation success depends on gaining accurate buyer personas and audience discovery. It is crucial to gain a comprehensive understanding of the customer at the beginning of the digital transformation, and AI is ideally suited to this mission.           

AI helps reveal consumer behavior trends, purchasing history and digital channel engagement to create a buyer’s detailed view. Today, buyers expect personalized experiences along their journey. AI is essential to quickly unlocking consumer insights to create and reinforce those personalized experiences. This way, companies can gain a competitive advantage through higher customer satisfaction, increased customer loyalty and more effective marketing campaigns.

2. Dynamic Analytics

As already mentioned, collecting useful data is essential to digital transformation strategy. But what powers the success of the strategy is how that data is capitalized. AI processes thousands of data points to gather insights and identify trends in real time. This is how companies can accurately pre-empt consumer needs rather than react to consumer actions. With these insights, companies can create highly personalized experiences for their buyers and make data-driven decisions to develop more effective marketing campaigns. Success is reflected in significant savings across budget allocation and in more cost-effective media and advertising purchases.

3. Growth and Profitability

AI helps businesses profit through their digital transformation by increasing operational efficiency, mitigating risks and accelerating growth and innovation. For business leaders, marketing, sales and customer service are the top three areas where AI can make the most impact. According to Accenture, AI can increase profitability in multiple industries by an average of 38 percent by 2035. By that same year, AI will boost additional revenue by $14 trillion!

AI-Driven Automation for Customer Service  

Digital transformation that incorporates AI is redefining and revolutionizing the end-user experience in countless ways for both B2B and B2C organizations. The new capabilities offered by AI-driven Conversational RPA for Customer Service can auto-resolve 65 percent of user requests and reduce issue resolution time by 90 percent—improving customer satisfaction scores by 80 percent. Sophisticated workflow automation integrates AI and RPA to automate customer workflows across product support, upgrades, troubleshooting, case management and more. The horizon for new revenue streams, cost-savings, and productivity is nearly unlimited.

Redefine your employee and customer experience with the world's first AI-driven service desk.

 Kim del Fierro, VP of Marketing at  Aisera 

Artificial Intelligence (AI) and the Internet of Things (IoT) are the two hottest buzzwords in the world today. The two, together with big data, dominate all tech conversations and business talks. But when people talk about the benefits of IoT, they often end up talking about what AI will contribute. The two always complement each other since AI’s intelligence is needed to transform the data collected by IoT devices into actionable insights that can be used for various purposes.

AI and IoT

Internet of Things (IoT) refers to sensors or devices (things) connected to the internet and can gather and process data into a form that can be used to make smart decisions. Some smart devices that you might have come across include wearable devices, door locks, and smart cars, among others.

Artificial intelligence, on the other hand, is machines' ability to mimic human intelligence, making machines think and act like humans. 

Artificial Intelligence meets the internet of things

Many professionals say that there is no IoT without AI. Although the statement is not entirely true, it just shows the importance of AI in IoT. However, IoT is more about data collection, which gives organizations actionable insight that enables them to improve their productivity, build better products and enhance their processes.  

Combining AI and IoT gives us AIoT, which blends the intelligence in AI with the ability to smartly collect data and process it to be used in decision-making. While IoT collects all data and transfers it to storage facilities, it is mainly the cloud- AI helps simulate machines and act or respond to it accordingly. In devices such as thermostats, sensors collect data on the environment but do not decide on the action needed. Rather, the AI component built into the IoT devices helps make intelligent decisions that allow the air conditioner to adjust the room temperature accordingly, based on the data received. Another example is the sensors used to manage equipment in manufacturing companies. The sensors collect data about the condition or the state of the equipment, and the AI component decides if the equipment needs maintenance or not. If it needs maintenance, it alerts the user, who informs the mechanic on the same. ASHRAE is an example of companies that are known for such intelligent air-conditioners.

Benefits of blending AI with IoT

• Enhanced customer experience

Blending IoT with AI can be of great importance because companies can take advantage of many AIoT devices to learn about the users and their preferences and adjust their products or services accordingly. Some smart devices such as thermostats can adjust temperatures by themselves without any human intervention.

• Cut costs

In the industrial setup, machine breakdowns can cause a lot of losses to any organization. They are a common problem that is often unplanned and leads to thousands of dollars an hour if not properly planned. However, the collaboration between IoT and AI can help monitor equipment and identify patterns that can help predict possible machine failures in time. IoT takes advantage of AI’s ability to sift through data and draw patterns on possible machine failure.

• Development of new products and services

AIoT can directly help in developing new, powerful products and services. This can be achieved by collecting, processing, and analyzing data to make informed, human-like decisions depending on the situation.

AI and IoT can be applied in traffic management, self-driving cars, smart homes, body sensors and can also be used in robots for manufacturing. As IoT sensors and devices continue gaining fame and affordability, more and more organizations will adopt them to reduce costs, increase income, and improve processes. The massive amounts of data generated by various devices mean that humans will not work efficiently on it. This is where AIoT will come in handy.

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

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

• Vaccine development

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

• Self-driving cars and robotaxis

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

• Quantum computing

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

• AI chips

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

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

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

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

• Remote work management

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

• Virtual reality will enhance collaboration

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

• Augmented reality will add digital layers of information

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

• Employee training with 3D modeled interaction

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

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

As the number of internet-connected sensors continues growing due to increased applications in cars, planes, buildings, and home devices, businesses generate vast amounts of data. According to a study, there will be more than 50 billion IoT devices in the world by 2025. This is up from 9 billion in 2017. With this significant rise in data, tapping it to extract useful information is becoming a big challenge that traditional methods cannot meet. This is where machine learning (ML), one of the subsets of artificial intelligence (AI), comes into place.

With the wave of technology on the rise and the need to gather insights, customers are increasingly demanding devices that can give them insights and at the same time keep their data safe. IoT's AI-powered capabilities ensure that this is achieved by transforming data, enabling analysis, visualization, and embedding across different devices, gateways, data centers, and ecosystems. IoT is a subset of edge intelligence. An intelligent IoT system comprises mechanical and electrical components, sensors, processors, storage, and software. It also has ports, antennas, protocols, and in-built analytics to train and run AI models.

For IoT to be successful, tens of billions of devices sitting at the edge in homes, offices, factories and oil fields, and automobiles need to be connected. Machine learning will play a critical role in the deployment of IoT applications. It will unlock the potential of IoT through its ability to wring insights from data. Machine learning, a subset of AI, enables IoT devices to identify patterns and detect anomalies automatically in intelligent sensors and smart devices. Connecting IoT with machine learning will lead to the development of appliances such as refrigerators and others that will automatically detect anomalies such as temperature, pressure, sound, and humidity.  

Unlike the traditional business intelligence tools, machine learning approaches are highly efficient and can make predictions that are up to 20 times faster and incredibly accurate. Machine learning also can enable organizations to avoid unplanned downtime, enhance operational efficiency, increase innovation and promote management of risks. Predictive capabilities of machine learning are beneficial in manufacturing setup.

By gathering information from different sensors in machines, machine learning algorithms can do calculations and understand the abnormalities and unusual starts in machines. Seeing when a machine needs maintenance work in advance is critical. It converts time and resources that would have been wasted into some dollars in spared costs. Some companies now use machine learning to predict with up to 90 percent accuracy when machines will require maintenance work, meaning massive cost cuttings. In various sectors such as oil and gas and industrial manufacturing, unplanned downtimes due to equipment breakdown can be costly. With analytics, predictive maintenance can mitigate losses of unplanned downtimes.

Just as machine learning and AI can help predict machine failure, they can also improve operational efficiency. It can predict operating conditions and identify parameters that need to be adjusted to maintain the correct results. ML can analyze constant data streams to detect anomalies that are invisible to the human eye or simple gauges. Machine learning helps find erratic insights, therefore, increasing profitability through operational efficiency.

Connecting IoT with AI can also lead to the direct creation of new products and services. In logistics and transport industries, fleet management has benefited immensely through AI, which monitors data points in trains, trucks, and even planes to find better routes and perform scheduling through data. This also reduces time wastage and downtimes.

Once IoT is connected to machine learning, companies are given an edge through trend analysis which will improve performance.

Artificial Intelligence is already impacting the world in many ways. Many scientists have even compared it to the discovery of fire, although most of them differentiate the two on the creativity they need. While AI was a preserve of a few AI researchers and digital companies such as Google, Amazon, and Microsoft, that is no longer the case. AI has entered almost every industry, with nearly everyone now affected by this technology. Machine learning is one of the subsets of AI. It is based on algorithms that allow systems to learn on their own without relying on humans. 

Although AI and machine learning have been here for almost a decade now, there are things we keep learning about this technology each day. Here are some things you might not know about machine learning.

• Machine learning is defeating humans in their own game.

Modern machine learning has become sophisticated in many ways. AI can now solve various problems, including the Rubik's cube in less than 40 seconds and beating champions of strategy game Go. This capability of AI and machine learning, in particular, shows that computer is slowly edging out humans in some areas that they dominated in the past. It is just a matter of when and not if machines will beat us in things that require intuition and deep thinking, such as chess. 

• Machine learning has gone beyond North America.

Although North America was the first to take advantage of AI and machine learning, other regions are quickly taking advantage of this technology as well. For example, countries in Europe are using machine learning in industries such as banks to perform things such as statistical modeling, thus improving their profits and sales. Recommendation engines for clients have become crucial for retailers and small and medium-sized organizations in targeting their customers. Machine learning algorithms also help in analytics and talent management in the US as it is in other countries in the world. 

• Machine learning is helping traditional industries collect fresh business insights.

Machine learning (ML) has become an effective tool for gathering insights and using analytics to process information used to make crucial decisions. In sports, for example, ML tools are vital in digitizing games and distinguishing between good players and tactics from the bad ones. FOR EXAMPLE, the US National Basketball Association relies on machine learning in scheduling their games and allowing coaches to distinguish between bad and good shooters. From this, it is clear that AI is helping leaders make better decisions through advanced algorithms. 

• Machine learning is helping create art.

While machine learning cannot do everything that humans can, it is slowly making way into some of the most complex areas. Although machines are not capable of original thought like humans yet, they are slowly getting there. It has proven that it can create music, art, and even literature and some of the advanced artificial neural networks are capable of producing some unique things. By learning and constantly cross-examining works of art, AI, through machine learning, can create powerful pieces of art that take an effort of experienced professionals to distinguish from those done by humans. 

• AI can predict earthquakes.

Although many people think AI and machine learning could soon render them jobless, they are far more helpful in many ways. AI can save lives that natural disasters could have otherwise claimed. An example is The National Center for Research on Earthquake Engineering under National Applied Research Laboratories (NARLabs), which has developed a machine-learning algorithm based on AI to predict an upcoming earthquake and its magnitude in as little as 3 seconds. This has made it possible to warn people of the forthcoming quake in time. This literarily saves the lives of thousands of people. 

While these are some of the things you most likely didn't know about machine learning, there are many more capabilities and knowledge you don't know yet. In years to come, almost everything around us will be automated, and AI's reliance will increase more than ever. This calls for the acquisition of knowledge and learning to better usability and implement machine learning and other AI-related technologies.

Artificial intelligence (AI) and Machine Learning (ML) are now considered some of the most groundbreaking inventions since the internet. We used to watch AI in movies and science fiction books, but it has now become a reality that we interact with in our daily lives. AI has become so famous to the extent that almost all technologies being invented today have some element of AI or machine learning. ML, a subset of AI, is an implementation of some abilities of humans. It enables people to carry out certain tasks by collaborating with smart software.

Here is how machine learning is creating a global shift:

• Self-driving cars and automation of transportation

Self-driving cars and automated transportation has caught the attention of automakers in recent years. It is a battle-ground among companies such as Tesla, Toyota and other tech companies such as Google and Baidu. Commercial airplanes are using a combination of GPS, computer systems and sensors to track a plane's position during flight. Self-driving vehicles are among the key adopters of ML. These cars use ML on the road to avoid obstacles and process information regarding traffic patterns and rules. AI-powered cars are now considered safe than human-driven cars.

• Personalized digital media

Social media has become a destination of choice for marketers and advertisers. For this reason, social media companies now use ML to help advertisers make decisions based on the preferences of their target market. From feeds that we see to social media notifications we receive, ML is used to curate the content that is shown to the customer. Machine learning examines our past behavior, web searches and interactions and customizes content based on what we like to improve our experiences. An example of such is YouTube, Facebook and Spotify, all of which use recommendation engines for their respective customers.

• Healthcare

Machine learning is being leveraged in healthcare to enhance patient diagnosis. They can predict patient’s health issues based on their history and other factors. ML algorithms are now used to detect tumors in radiology scans and detect cancerous cells. Furthermore, computers help doctors classify images from scans and detect cancer more accurately than human professionals. Machine learning algorithms are also aiding researchers in looking for treatment for certain diseases in a faster and efficient manner. In general, hospitals will soon put the well-being of their patients in the hands of AI. Some hospitals are currently using machine learning to treat patients to reduce accidents and reduce the cases of hospital-related illnesses.

• Banking

Machine learning is changing the banking industry. With the growing number of people using banks, it would be difficult for employees to go through transactions of each one of them daily. Using location data and customer purchase patterns, machine learning can help banks and financial institutions offering credit services to identify fraud right on time. The machine learning-based anomaly detection algorithms monitor customer transaction requests and alert users of potential cases of fraud.  With this, banks can arrest the situation before it gets out of hand.

• Smarthome and home security

Machine learning is becoming the best home security alternative for home security. Many homeowners look for AI-integrated cameras and alarm systems to guard their property and homes. These high-tech systems use facial recognition technology software and machine learning to build a catalogue of frequent home visitors and instantaneously detect uninvited guests. The newest smart home systems can even track your kids and notify you when they are at home. They can also call emergency services autonomously in case of any issue. With digital assistants like Alexa and Amazon Echo, different aspects of smart homes can be voice-controlled.

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

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

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

Let me explain why...

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

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

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

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

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

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

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

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

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