Artificial intelligence (AI) and machine learning (ML) have transformed innumerable fields and sectors. These two technologies are leading the way in the automation and optimization of processes. Furthermore, they have created new business opportunities and made the existing ones efficient. AI and ML have helped humans solve problems faster than traditional computing platforms. These technologies are not only limited to the corporate world. They have also made significant progress in helping in other areas such as research and development. In fact, advances in AI are becoming a critical part of space exploration, having shown massive potential in designing missions, among other areas in space exploration. Here are some ways AI can help in space exploration venture.

• AI-enabled astronaut assistants

You might have seen home assistants such as Nina, Google Assistant, and Alexa. Well, although the astronaut assistants might be different from the Alexa and some other assistants you know, AI-enabled robotic assistants for astronauts might soon be a reality. Researchers are working towards such assistants to help astronauts in a variety of ways.  Some of the areas that these assistants can help are sensing leakages, detecting dangers such as changes in the spacecraft's atmosphere and increased carbon dioxide, among others. Cimon, an AI assistant that was flown to the International Space Station in 2019, has been tested, and it showed the potential of reducing the workload that astronauts could have done.

• Designing and planning missions

Mission planning and designing are not easy, but artificial intelligence can come in handy and make these processes more manageable. New space missions rely on the past knowledge collected from previous studies. However, the past information may sometimes be limited or inaccessible fully. This will affect the flow of information and sharing of knowledge. If the information from all the past space missions can be availed to anyone with authority, space missions can be simplified. AI can simplify things by answering complex questions regarding space exploration, which can help plan, design, and manage new space missions.

• Processing satellite data

Satellites used to observe the earth from space generate large and critical amounts of data. This data is received on the earth by the ground stations, often in chunks and over a longer period. With this fragmentation, data has to be pieced together and analyzed to draw meaning. AI algorithms can analyze data in detail and capture patterns or images that a naked eye cannot see. Since data is always large, traditional methods of processing data are often inefficient and tedious. Scientists are now relying on AI to process data and monitor the health of satellites.

• Navigation systems

Navigation systems such as Google Maps that use GPS help us on earth’s navigation. However, there is no such system for space. In other extraterrestrial bodies such as Mars or even the moon, navigation is different and complex because we lack a navigation system. However, millions of images taken by spacecraft and satellites can be used. In collaboration with Intel, researchers from NASA developed an AI-based intelligent navigation system that can be used to explore planets. The system was trained using data from millions of images from different past missions.  This indicated that AI could become a critical element for the future of navigation systems.

Although the space exploration industry is still in its baby stages in the use of AI applications, the few areas where AI has already been used have shown massive potential. There is indeed a lot of uncertainty pertaining to adopting AI and other high-end technologies. However, the truth is that it will improve the lives and accuracy of space explorers.

Artificial intelligence (AI) is changing every industry but is fast proofing to be a boon for modern workplaces. It is changing how we live our lives and how we work and perform various tasks. AI can help handle mundane and repetitive tasks across workplaces, thus freeing up workers in different departments to handle other problems that are complex and impactful to the running of organizations. In other words, AI is giving modern workers time to focus on the most engaging parts of their jobs while reducing the cost, enhancing productivity, and improving effectiveness. With this impact, you will soon see AI taking over almost every department in organizations while AI machines will increasingly take over some tasks that humans currently do. Here are some recent advances in businesses that are currently benefiting from the recent advances in AI:

• Recruitment and onboarding

AI is proving useful in hiring individuals. AI-enabled machines are playing a critical role in ensuring that the right people are given the job. Through AI pre-screening of candidates, only the suitable ones are invited for the interviews. This is becoming a common practice in large companies and thousands of hires every year. From thousands of applicants who apply for a job, only suitable candidates are selected. Pymetrics is an example of a company offering tools used to assess candidates before they are interviewed. Montage, another company offering similar services, claims that most Fortune 500 companies have embraced the use of AI-driven interviewing tools. These tools enable them to conduct interviews with little biases.

• On-job employee training

Once employees land on the job or role of their dreams, learning does not end there. Learning is a life-long process in the workplace because new things keep coming up. With AI, employers can identify the knowledge gaps among their employees and develop a curriculum accordingly. AI assists organizations transfer knowledge and skills from one generation of workers to the other and ensure that valuable experience that the retiring employees had is retained. Honeywell has developed tools that use augmented and virtual reality (AR and VR) and AI to capture work experiences and extract lessons that can be passed to new employees. AI ensures that only the right knowledge is passed down to new hires and that the tradition of the organization moves a notch higher.

• Surveillance

Most large organizations are using AI tools to monitor employee activities and assess their performance. This includes surveillance of the content of the emails to determine the engagement levels of the employees. Others go as far as tracking the devices used by the employees to determine the stress levels of employees and their habits. While this can be a good thing in understanding employees, it makes so many employees uncomfortable in their work. As intrusive as they are, it allows organizations to monitor and gather employee interactions for tailor-made solutions to be made. It can even help protect employees from unwanted behaviors and bullying.

• Workplace robots

Robots have become commonplace in many workplaces today. Physical robots that work autonomously are helping manufacturing and retail companies in their warehouses to move products around or package them. Although this technology is in its early stages, it has proven helpful for companies such as Amazon, Alibaba, and Walmart. Autonomous delivery robots are also gaining fame, with companies such as Segway now developing robots that can help in workplace delivery directly to the desk. With these developments, the workplace will not be the same again with AI-enabled robots as the movement of employees is limited. AI-enabled security robots such as those developed by Gamma 2 are also becoming common to keep commercial properties safe. Acquiring these robots will enhance efficiency and reduce the cost that could have been incurred in hiring humans.

The U.S. Army, Social Security Administration, Department of Veterans Affairs, Blue Cross Blue Shield, The Brooklyn Hospital Center, universities from UCLA to NYU, and hundreds of major banking systems and offshore oil rigs all have the same secret weapon in their arsenal – a remote A.I. network wizard named Uplogix. 

Unlike The Wizard of Oz, there is no man behind the curtain, rather, there is an artificial and automated intelligence that sits in every rack of an organization’s computer network, assessing and correcting even the slightest network blip, quickly and in real time from a remote location. For the large organizations listed above who use Uplogix for their network troubleshooting, it eliminates downtime and margins of human error. For IT professionals, it’s a trend worth taking note of. Here’s why:

Where large corporate or government data and revenue are concerned, there is no room for human error. There is also little room for human commuting when time is of the essence, when data is threatened, or when 8 figures in revenue is on the line.

According to Uplogix CEO, Lisa Frankovitch, “56% of network outages are caused by human error, but 70% of IT tasks are still manual. That math doesn’t work. Add to that, over 30% of IP manager troubleshooting makes IT professionals less productive because they end up neglecting more strategic network building tasks. Uplogix supports and frees up IT professionals for more productive and creative work.”

Plainly put, humans are ineffective troubleshooters, but human minds are ideal for creating, innovating and building. This logic dictates that going forward, IT troubleshooting should be left to automated, closed loop, out of band Artificial Intelligence-based products like Uplogix, while building out networks should remain in the hands of human beings.

Dubbed a “network administrator in a box,” Uplogix is a highly secure intelligence that anticipates and fixes computer network issues remotely, 24/7, using patented alternate route, out-of-band technology. Uplogix sits in every rack of computer gear, running network health checks every 30 seconds on large enterprise networks. It deploys remotely to anticipate, triage, and fix even the slightest network blip, seamlessly, and in real time. Everything is stored and encrypted on hard drives, which none of its competitors can do.

Uplogix never goes down, because, unlike other out-of-band network systems, Uplogix operates on an alternate path.

“Uplogix was founded on the observation that the problem with network management is that it relied on the network itself to operate.” – Lisa Frankovitch, CEO of Uplogix.

 Uplogix CEO, Lisa Frankovitch, about Uplogix and the future of I.T. Management and I.T. Troubleshooting.

Artificial Intelligence has the potential to greatly improve the human experience, from improving medical diagnoses to alleviating repetitive and banal tasks to helping tackle climate change. Today, we already see algorithms in use in recruitment, e-commerce, banking, and the military, just to name a few. However, just as any new technology that brings opportunities, AI also raises new questions. How do we make sure decisions made by AI aren’t biased towards some people? How do we prevent our networks being used for unethical AI applications? And how will AI impact jobs in the future?

We’ve seen no shortage of controversy when it comes to AI. In just the past year, we’ve seen robo-editors confuse mixed-race singers, an algorithm appearing to penalize top-performing students from disadvantaged backgrounds, and an AI-powered tool appearing to favor white faces over black faces. Companies need a plan for mitigating any risk associated with AI. How should organizations develop and implement AI products without falling into ethical pitfalls along the way? The answer boils down to data quality and explainable AI; having the applications able to demonstrate what data was used, knowing who trained the AI training criteria, and making it as clear as possible how the AI came to its answers.

Data quality fuels AI quality

Organizations must remember that AI is not unethical per se, but is limited by the information it is fed and the objectives is given. AI algorithms are trained through a set of data that is used to inform or build the algorithm. If an algorithm is making inaccurate or unethical decisions, it may mean there was not sufficient data to train the model. A new concept of AI quality alongside data quality is fundamental to ensure compliance and ethical risks are spotted in time and properly addressed. It is essential that algorithms operate with enough high-quality data and within a well-defined and unbiased context.

Data and AI quality start with the people that do the work. A workers’ experience significantly impacts the work they deliver, and leadership should ensure developer teams and data scientists collecting and analyzing the data are well-rounded and diverse. Unfortunately, businesses around the world are struggling to establish this precedent. According to research, 80 percent of AI professors are men, and women AI researchers at big tech firms including Facebook and Google represent only 15 percent and 10 percent, respectively. When we don’t take steps to mitigate this, the results can be poor feedback loops that can trap people based on their origins, history or a stereotype.

Regular training is also essential to data quality, as designers need to be aware of all the implications that go into collecting and analyzing quality data. Depending on the difficulty and complexity of a task, customized training may be required to ensure the continued skill development of the data worker, which results in higher quality work. For simpler tasks, minimal training may be enough to deliver quality results.

Optimizing data quality

Building AI systems that battle bias is not only a matter of having more diverse and diversity-minded design teams. It also involves training the programs to behave inclusively. Many of the data sets used to train AI systems contain historical artifacts of biases. If those associations aren’t identified and removed, they will be perpetuated and reinforced. While AI programs learn by finding patterns in data, they need guidance from humans to ensure that the software doesn’t jump to the wrong conclusions. A “check list” should be created, supported by leadership, to guide data scientists and AI engineers and more broadly ‘Data Citizens’, 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.

No element is more essential than quality training data, which refers to the initial data that is used to develop an AI model, from which the model creates and refines its rules. The quality of this data has profound implications for the model’s subsequent development, setting a powerful precedent for all future applications that use the same training data. Data labeling is the backbone of good AI training, because, in a simplistic way, it ‘tells’ the model what a good or bad outcome looks like.

An AI model can learn from the labeled data and when such scenarios come again in real life, it can give the most suitable results. The best process for labeling quality training data is built for scale, with tight quality controls and clear parameters for task precision.

Establish a system of governance

Whilst AI ethicists are important to make AI right, they can’t succeed without the proper implementation standards and technology supports. Establishing a system of governance with clear owners and stakeholders for all AI projects is crucial. All involved parties have to play their role to make sure AI is not only successful, but fair and controlled at the same time. This includes monitoring data regularly to ensure bias is not creeping in and the models continue to operate as intended. Whether it’s the data scientist or a dedicated hands-on AI ethicist, someone has to be ultimately responsible for AI policies and protocols.

The call for AI ethicists is only growing as technology leaders publicly acknowledge that their products may be harmful to employment, privacy, and human rights. Several organizations have taken the lead on the actual hiring, while others have made recommendations to hire for this role under different titles like chief trust officer, ethical AI lead, or trust and safety policy advisor. Regardless of the title, a clear role should be established to make sure output and performance of AI are within a given ethical framework.

Turn statements into action

No data model is perfect. That means human beings need to constantly monitor the systems looking for potential ethical problems. Mere accountability isn't enough; all parties involved need to be actively tracking the AI's actions and interactions with humans and making adjustments as necessary to ensure the technology does not cross any ethical boundaries.

Organizations must identify the boundaries, how to enforce them and even how to change them, if necessary. We as professionals and individuals are expected to behave and act ethically and the same should be expected of AI. Just as we’ve always had a need for robust governance with standard analytics, we also have this responsibility with the data that feeds our AI systems — with potentially far greater consequences, as AI starts to ask the questions and define the answers itself.

According to Space.com, scientists believe that artificial intelligence can help them spot gravitational waves. 

Scientists hunting for elusive gravitational waves across the universe may be able to supercharge their discoveries with a new tool: artificial intelligence.

Read the article on Space.com

New artificial intelligence assisted technology can help detect colon cancer earlier, reports ABC Action News. 

Leading the way, right here in Tampa Bay, Largo Medical Center is now the first hospital in the country to offer a newly FDA approved technology to help doctors detect colon cancer early.

Read the article on ABC Action News

According to Indie Wire, artificial intelligence was used to recreate Anthony Bourdain’s voice for the new documentary about him.

“Roadrunner,” the Anthony Bourdain documentary from “20 Feet from Stardom” Oscar winner Morgan Neville, takes a page from documentaries like Alex Gibney’s “Sinatra: All or Nothing at All” in allowing the late chef and television personality to narrate his own story through the use of archival audio.

Read the article on Indie Wire

Startup Xembly uses artificial intelligence to make meetings more productive, reports Geek Wire. 

Nobody really likes work meetings. Pete Christothoulou thinks they’re such a problem that he’s launching a new startup to rethink the whole endeavor.

Read the article on Geek Wire

According to Forbes, many marketing firms use artificial intelligence. 

Leveraging artificial intelligence (AI) is now commonplace in marketing. Tools, platforms, and services put sophisticated audience targeting and segmentation tools at marketers’ fingertips, making it easier than ever to connect your products and services to customers. 

Read the article on Forbes

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.

Driving continuous improvements and efficiency is always a top priority for businesses. As the digital technologies keep advancing, we keep getting more data and intelligence than ever, which help us make a better decision. Our ability to use data collection, predictive analytics, and the internet of things enhance business operations in almost every industry. Workplace safety is the latest beneficiary of machine learning and artificial intelligence. From ensuring safety in processes, assessing the factory floors, or enhancing safety in warehouses, machine learning (ML) is a promising technology that could soon make things better. Here are some ways artificial intelligence and machine learning will help in enhancing safety in the workplace.

• Reducing accidents with enhanced accuracy

Work-related injuries cause businesses billions of dollars cumulatively every year. Most of these injuries happen on the floor of factories. Thus they are called work-related injuries. Sadly, over 70% of these injuries or deaths occur because of unwanted interactions with heavy machinery such as forklifts and other vehicles. With the dangers that these accidents pose to the income and lives of workers, organizations have come up with ways to minimize accidents.

An example is Warny, developed by Bigmate to enhance safety in places of work. It has vehicle collision avoidance, safety zone alert, and thermal analysis to warn people of the pending danger on factory floors. This system uses AWS services that include IoT and machine learning through sophisticated computer vision algorithms to protect people who handle dangerous machines, including forklifts and trucks, and manufacturing equipment.

• Enhancing safety during pandemics

In a pandemic such as the ongoing COVID-19 crisis, returning to the physical workplace has become a challenge that requires strict policies to succeed. Safety approaches such as wearing masks, temperature screening, and social distancing policies have been enforced to reopen offices and ensure the employees remain healthy. In organizations, manual screening of temperature can affect productivity and cause inefficiencies and bottlenecks in entrances and exits. However, with the advanced thermal imaging technology, elevated temperatures can be detected in real-time, and the speed is increased significantly.

Thermy, an AI-based solution, can scan 30 people per second, meaning more than 500 people can be scanned in a minute. This solution uses ML algorithms to check the changes in skin temperature and calculate it accordingly. This is something that manual temperature scanners cannot achieve. Thermy uses computer vision and data from an optical camera to capture the temperature and calculate the representation of skin temperature.

• Mitigating natural disasters

Natural disasters present a significant danger to organizations and employees. As such, solutions for predicting or detecting the disasters before they occur should be sought and used to avert losses and minimize deaths. Companies such as Bigmate have some of these solutions. Bigmate’s solution leverages computer vision and data detection technologies to detect natural disasters such as floods, tsunamis, and infernos by federal and state agencies in real-time. High-tech cameras fixed on aircraft wings are used to capture and collect data such as latitude, time, distance, altitude of disaster landmarks. Other critical information, such as speed, in the case of wind, is also measured.

AI technology is used to calculate information and understand where the incident is, geographically. The real-time data is sent to the meteorological department, where data is merged with population and location data. With this data at hand, safety and environmental organizations that respond to specific disasters look for resources and deploy them accordingly to safe lives and property before the disaster occurs. They can also use such data to prepare for the worsening of the disaster and evacuate the population to safer locations.

Artificial intelligence and machine learning are making their way into the business in a big way. Every time you speak to the executives of big or small companies, you will not end the conversation without hearing their plans for investment in machine learning (ML) or artificial intelligence (AI). One of the areas that have benefited immensely from ML/AI is human resource management. Although this sector has experienced significant changes in the past few years due to the evolution of technologies, arguably, none of them has transformed HR like AI and ML. Here are some ways that artificial intelligence and machine learning are helping in human resources:

1. Reduces bias in appraisals

The biggest challenge of any human resource manager during performance appraisals is to appraise employees without bias. This is often difficult because humans are naturally biased. With the help of AI/ML algorithms, analysis can be done beyond the usual spreadsheets through the execution of employee assessments via continuous appraisals. The AI/ML algorithms can be used to estimate the employees’ career paths and prepare them for the advancement of their careers. 

• Estimating employee morale

The human resource industry is leveraging AI and ML in the identification of employee patterns over time. Technologies such as facial recognition are capable of measuring employee emotions on a particular scale and even differentiating gender. The reports and data gathered can be used to develop solutions by deriving insights and acting on them. The insights can lead to the development of strategies to boost employee morale and enhance their potential in places of work. 

• Skill management

Machine learning is showing high potential in enhancing the management and development of individual skills. While this area is still in its initial stages, AI-based platforms can be calibrated to guide the development and management of individual employees without the intervention of human coaches. This not only saves time but also provides the opportunity for more people to be managed to grow their careers and remain engaged all the time. An example of the use of ML in skill management is Workday, a company that builds personalized training recommendations for employees based on the needs of the organization and the specifics of an employee. With machine-based feedback, individuals can learn a lot and grow into their jobs. 

• Streamline hiring processes

The hiring process is often difficult and employers end up getting candidates who are not up to their set standards. However, with AI/ML, every stage of the hiring process is enhanced by availing data given by personalized research tools. These tools and data allow organizations to find the best talent in the industry with the specific traits that meet employer’s desires. An applicant tracking software can help HR recruiters by analyzing numerous resumes and reducing the ambiguities encountered during recruitment. The AI-based software can analyze several resumes based on specified keywords, location, skills, and the applicant experience. 

• Payroll processing

Payroll processing is one of the complex tasks that HR managers have to face regularly in their operations. With the help of ML and AI, payroll processing and employee expenses management can be taken care of with the help of HR bots. With these bots, you may not need to spend too much time filling many forms that are necessary for the documentation of business expenses. Rather, you can just notify the bot, which will process everything and get your bills approved by your manager. This simplifies work for the HR personnel and enhances accuracy. 

Artificial intelligence and machine learning technologies are proving crucial for operating businesses. For the human resource aspect of the organization, it allows organizations and HR teams, in particular, to easily manage various day-to-day processes, overcome hurdles and assure operational efficiency.

Technology and science fascinate us, there are innovations and discoveries that push the boundaries of science every year and has made artificial intelligence a concept we are much familiar to these days. Although more of the usage of applied AI (applied machine learning), which obviously requires skills, resources, and knowledge going beyond data science present a challenge usually absent in the academic and scientific research settings.

Some examples of organisations succeeding and failing in integrated machine learning have been traced by Alyssa Simpson Rochwerger and Wilson Pang, in their new book Real World AI: A Practical Guide for Responsible Machine learning.

Here are the four key challenges that Rochwerger and Pang highlight in their book and how product leaders can avoid repeating failures in the world of innumerable machine learning strategies.

Defining the problem

It is to be acknowledged that "doing the right thing" is different from "doing the thing right". Defining the problem plays a major role in the choices you make for the technologies, data sources, and to breif people working on your project. So, to know the problem is a challenging task in itself.

There are high chances that many people fail just because they're trying to figure out the wrong problem, and hence they fail to account for all the variables, latent biases which are mostly crucial to a model's success or failure. A detailed definition of the problem will determine the kind of model, data, talent, and investment you need so that you can actually perfeom tasks with stunning accuracy.

In the era of machine learning, determining how well you want to solve the problem is also a part of defining the problem.

Gathering training data

Gathering and organizing the data needed to train the models in applied machine learning is again one of the major key challenges as well. We can consider this in contrast to scientific research where training data is usually available and the goal is to create the right machine learning model.

Public datasets are not useful for training models in many applied machine learning applications and for that you either need to gather your own data or buy them from a third party. Both the options come at no surprise but with their own set of challenges.

Verifying data quality and provenance is also crucial to the quality of machine learning models in cases where the data comes from different databases. You could end up producing a useless model if you are making assumptions about how the data you’re using got there, and so you need to highly accurate and updated with your work.

Maintaining machine learning models

What is a machine learning model?

Machine learning models are prediction machines that work on finding patterns in data obtained from the world and forecasting future outcomes from current observations.

So accordingly as the world around us changes, so do the data patterns, and models trained on past data gradually decay.

Moreover, this is a key part of any successful machine learning strategy. Making sure you have the correct infrastructure and processes to collect a continuous stream of new data and updating your models time and again is an important aspect as well.

Gathering the right team

Your models are going to affect people’s work and life, and your company’s bottom line as well in applied machine learning. The reason for a successful implementation of a machine learning strategy is an isolated team of data scientists.

Applied AI includes people from different disciplines and backgrounds working together and needing a cross-functional team irrespective of all these factors, where not all of them are technical as well.

This field also requires technical support beyond data science skills. There is integration of models into other softwares done by the help of software engineers.

Developing the right machine learning strategy

Although we have rounded up some of the major key challenges you'll face in applied machine learning. But you will still need more elements to make your machine learning strategy work better.

Rochwerger and Pang discuss about various strategies dealing with dilemmas, production challenges, security and privacy issues, and the ethical challenges of applied machine learning. They will provide you with a  plenty of real-world examples that show how you can do things right and avoid botching your machine learning initiative, and would be of a great help if you're wondering to begin today.

Machine learning and artificial intelligence are fast becoming common in many organizations today. Owing to their capability to turn around fortunes for businesses, many organizations have implemented machine learning projects in different areas of their operations. When it comes to estimating the cost of machine learning projects, many enterprises often face difficulties. However, this should not be the case. Estimating the cost of such a project should be straightforward if done procedurally. In a world where technology is highly in demand and dynamic, requirements often change midway through the project. This leads to development and feature changes, integration issues, and many others.

Regardless of the changes, the estimation should be made after identifying the type of project being executed. The first step you would undertake in the cost estimation is to define the scope of the ML project. Begin this with the basics and ask questions such as: What data is needed for training? What is the expected output? How will the quality of training models be measured? Will the output accuracy be acceptable? Which systems will be integrated into the ML system? Finding answers to these questions will give you a scope of the entire ML project.

Separate ML tasks from those that are not connected to them to allow accurate cost estimation. Doing this will eliminate other tasks that are not directly related to the machine learning project, but which are part of the system. Do so by identifying all aspects of the project that does not need AI-related skills. To achieve this, ask yourself questions such as: How will the data be fed into the ML models? How will the end-user interact with the system output? Is the data format ready to use? What other external sources need to be used? After answering all these questions, you can easily estimate the costs with little error.

After the separation of tasks, you need to choose the development approach which may have a bearing on the cost of your project. Doing so is simple, mainly if you are a professional in this field or have worked on such projects in the past. You will need to estimate the cost of data since this is often the most important asset in any machine learning project. Machine learning projects are data-hungry and cannot function as they could without the right data. A study by Dimension Research indicates that 96% of organizations face issues related to the quality and quantity of training data.

There is also the cost of research. Any serious ML project entails extensive research before production. Look into the members of the project research team. According to Dimensional Research, an average team has about 5 members. Take into consideration the number of outsourced team members, which can be about 3 members. Additionally, estimate the cost of production, which includes the cost of cloud compute or data storage, integration costs (API development, documentation), and the cost of maintenance. There is also some other infrastructure that is necessary for the project to operate effectively. Maybe the lowest cost you will incur here is the one associated with cloud computing. Lastly, there is the opportunity cost.

In conclusion, ML projects are hard to estimate. However, having the right analysis of all the non-AI costs can clear a lot of things and can ease the process. Machine Learning projects vary depending on the implementation approach. Each approach to implementation has different costs, time, and cost probability. The best way to get around these challenges is to estimate the cost of every aspect of the project, one step at a time.