Artificial intelligence (AI) and machine learning (ML) are two key technologies that have taken over almost every industry today. From self-driving cars, consumer products to industrial systems, smartphones and other home appliances, these technologies are developing faster than ever imagined. Despite the rapid developments, AI is still in the nascent stages and still faces major challenges. In such early stages, failures and mistakes are to be expected. While these mistakes or failures are understandable, some can be fatal. Here are some areas that AI still faces problems.

1. Challenges in image recognition

Image recognition is one of the areas that AI is expected to dominate in the coming years. For this reason, it is one of the hot topics when AI is mentioned with regard to data science. However, the use of AI in this area still encounters challenges. An example is in 2015 where Google’s image recognition feature was launched in its Google Photos application. This application powered by AI was designed to identify specific people or objects in images. However, the images of black individuals was tagged as “GORILLAS,” which is not only wrong but also disgusting. This forced Google to apologize after this was exposed on Twitter.

2. Ethical dilemma in the military application of AI

Over the past few years, scholars have raised concerns on the challenges of AI and ML to ethics. This has led to numerous conferences and talks dedicated to the ethics and dangers of AI systems and their future. For military applications, AI-based autonomous and semi-autonomous weapons such as sentinel guns that work without human intervention have created a dilemma on whether allowing machines to pull the trigger is ethical or not.

3. Data Privacy and security

Privacy and security are still leading challenges in artificial intelligence and machine learning. Since machine learning and AI models are trained using data generated from millions of sources and belong to many people, the privacy and security of this data are still a significant concern. There are chances that some bad actors can use this data for sinister purposes. With such massive data pouring from different locations, AI systems may leak data belonging to unsuspecting individuals.

4. Self-driving cars still ran the red lights during the real-world testing

Uber, one of the most popular transportation companies globally, is among the leading adopters of self-driving cars in the world. In 2016, Uber went through lots of trouble when conducting a test of self-driving cars in San Francisco without getting the approval of the state of California. Reports indicated that Uber’s autonomous cars ran six red lights in the city during the numerous test drives conducted. This forced the driver on any of the occasions to take over the wheel. The situation could have been worse had there been no driver. According to the report issued by Uber, the violation resulted from the driver's error. However, the internal documents revealed that some vehicles were self-driving when they ran red lights at busy city crosswalks in daylight. This is one of the mistakes that AI still makes. At this point, there is a raging debate regarding the ability of self-driving cars to observe traffic rules. 

Some of the challenges of AI appear depressing and devastating. However, there is still a lot of room for improvement, which can be possible if the industry professionals and stakeholders can work collectively to bring about these changes. Furthermore, there is a need to upskill the next generation of engineers in cutting-edge technologies. This will allow them to stand a chance to work in the fast-paced environment determined by technologies such as artificial intelligence.

Artificial Intelligence (AI) and machine learning (ML) are becoming household names in the world today. After taking over the tech industry, these two are among the most talked-about technologies in our homes and business circles. They are helping businesses achieve goals and make critical decisions. With the help of these technologies, companies can create innovative products and services. As we begin a new year, AI and ML will see several breakthroughs. Here are the top ML and AI trends you should watch in 2022.

1. AI will play more role in hyper-automation

Hyper automation, the process where advanced technologies are used to automate tasks, is fast gaining momentum. Companies nowadays are working with a lot of data which demands advanced technologies to extract information from massive amounts of data. This is where automation comes in handy. The increased role of big data analytics in corporate decision-making makes AI and ML algorithms necessary to sift through massive amounts of data and draw patterns that can be used to make decisions. Data science can now be found everywhere, and data science tools will become increasingly available moving forward.

2. AI and ML will be used more in Cybersecurity

Cyberattacks have increased in number and severity than at any other time in history. With this advancement, AI and ML technologies will play a crucial part in scanning and fighting attacks. With the help of AI and ML, organizations are now developing new methods of automating cybersecurity. In addition to helping improve cybersecurity, it will also power up the cloud migration strategy while enhancing performance. In fact, cybersecurity use will reach 38.2 billion by 2026. ML will be used in cybersecurity data clustering, classification, processing, and filtering. AI can analyze past data and present solutions for the present and future. Based on the results from the analysis, systems will offer instructions on various patterns while also detecting threats based on the past behavior of malware.

3. Machine learning and IoT

The linkup between machine learning and the internet of things (IoT) is one of the trends that tech professionals are waiting for the most. The link between these two will impact the use of 5G, which is now being implemented across the world. As 5G comes with more speed, IoT devices will connect better via the internet. As the number of connected devices increases, the amount of information shared between devices will also increase. This will demand better data processing algorithms, which are made possible by machine learning. ML will ensure there are fewer errors and increases the speed of communication between devices.

4. ML in business forecasting and analysis

Business forecasting and analysis will be easy with the application of technology than using traditional methods. It increases accuracy and leads to advanced predictions and forecasts. Companies in the financial sector are using AI to forecast demands for different currencies depending on the conditions of the market and the behavior of the customer based on patterns shown by data in real-time. This will help improve accuracy and make the right decisions regarding supply and demand.

5. Augmented intelligence will rise

The collaboration between humans and machines to enhance cognitive performance will be useful to organizations. According to Gartner, 40% of infrastructure and operations teams will embrace augmented automation by 2023 as they seek to improve IT productivity. This will increase the productivity of digital workers by more than 50% by 2022. Augmented intelligence will help platforms gather all types of data, including structured and unstructured, from different sources and present it to the customers. Some sectors such as financial services, retail, healthcare, and travel have already taken advantage of this technology.

Artificial intelligence (AI) and data-related jobs are the biggest labor market winners in 2020 going forward. With the evolution of machines and the increasing automation that grew faster than anticipated due to the COVID-19 pandemic, AI and big data specialists will be in high demand. A 2018 World Economic Forum (WEF) report titled “The Future of Jobs 2018” stated that machines and algorithms are expected to create about 133 million new roles while causing displacement of about 75 million jobs by 2022. Accordingly, the report indicated that over 50 million net new jobs will be created in the next few years. Although the pandemic has substantially affected many things, positive job growth in this sector is expected, and so is the shift in quality and location of the new roles. The pandemic has increased remote jobs as companies seek contractors who will get the job done remotely.

WEF estimates that machines will perform more current work tasks than humans by 2025 compared to humans. This will significantly impact the global workforce and working arrangements. Although there is a notion that AI will eliminate more jobs than it creates, the truth is the opposite. The only challenge is understanding how this paradigm shift will affect the employment sector and plan accordingly for the future. The preparation for this needs to involve planning for the emerging roles and developing strategies at the executive level to handle the surge in AI-related jobs.

According to the WEF report, 54% of the employees in large organizations would need to upskill to fully harness the growth of opportunities. Sadly, most of them are not prepared because only half are planning to train only employees in key roles, while one-third are planning to train at-risk workers. At the same time, nearly half of all companies expect their full-time workforce to reduce by 2022 because of automation. However, 40% of the companies expect to increase their workforce, while more than 25% expect automation of tasks to create new roles in organizations.

In 2021, AI augmentation will generate more than $2.5 trillion in business value while recouping over 6 billion hours of worker productivity. Other industries such as outsourcing are experiencing a fundamental change in business models with reinvestment of cost reduction caused by increased productivity. Businesses will need to take an active role in supporting the development and reskilling of their workforce. On the other hand, individuals also need to be proactive and ensure lifelong learning, while governments need to create an enabling environment where learning and the adoption of new modes of work take place.

With this kind of prediction and forecast, there will be a major disruption. However, the key concern is the challenges caused by the unprecedented increase in the adoption of AI. The most concerning challenges are the skill gaps and the redundancy of workers. Skill gaps occur if surplus jobs are left unfilled because workers lack skills and training to take up these positions. On the other hand, mass unemployment caused by major shifts in the job market can adversely affect economies due to loss of productivity and overreliance on government assistance programs. These are the reasons why all stakeholders must engage in training the workforce, preparing policies, and investing in AI-related education. Training should focus on new technologies and fast-growing job markets across all industries, such as data analysts, software engineers, and IoT specialists. Although the WEF report is just a single proof that AI is the future of work, it sheds light on the need to prepare for more developments in the near future.

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 can be used by businesses for things like customer service and predictive analytics, reports Entrepreneur.

Artificial intelligence has the power to transform your business. It can be used for everything from customer service to sales, and it's easy to implement as well. Artificial intelligence is already being used in some industries, but it's just starting to become mainstream in small business.

Read the article Entrepreneur

According to the Advocate, AI could help Lousianna communities prepare better for flooding. 

Using artificial intelligence and machine learning, LSU professor Hartmut Kaiser is joining up with a team of scientists to help communities near the coast better prepare for flooding.

Read the article The Advocate

The NFL and AWS challenged data scientists to teach computers to detect head injuries in NFL game footage, reports NFL. 

The National Football League (NFL) and Amazon Web Services, Inc. (AWS), today announced the results of its artificial intelligence competition, which challenged data scientists to teach computers to automatically detect players involved in head impacts from NFL game footage

Read the article NFL

Artificial intelligence is still a long way from human level intelligence, reports CNBC.

Machines are getting smarter and smarter every year, but artificial intelligence is yet to live up to the hype that’s been generated by some of the world’s largest technology companies.

Read the article CNBC

Artificial intelligence (AI) is changing many aspects of the business. It allows business owners to offer a personalized experience to their customers. With the potential of this technology, patterns of information can be identified, including customers’ buying history, their preferences, credit scores and other common trends. With the pandemic ravaging the world today, businesses are adopting AI faster as the urge to automate processes continues gaining momentum. Here are some trends and innovations that reflect how AI is changing businesses.

Economic growth will drive enterprise IT spending

About two years after the pandemic began, the global economy has faced many challenges. In countries including the US, Germany, China, South Korea and others, it is expected that the economy will recover in 2022, despite the emergence of the new variants of the pandemic. Subsequently, IT spending is expected to go up to $4.072 trillion, an 8.4% increase compared to 2020. Out of this, businesses are expected to invest heavily in AI as they seek to automate services. The global spending on AI could soon reach $90 billion.  

The global labor market will see a significant change

Since the last quarter of 2019, job openings in the US have increased by 33%. On the other hand, unemployed individuals hit 8 million in August of 2019. This could be explained by the change that the COVID-19 pandemic has brought to many sectors of the economy, including different industries such as hospitality, healthcare and education. In the meantime, the demand for  IT workers has gone up, reaching over 25,000 towards the end of 2021. These factors are influencing the trends in AI this year and beyond.

Cybercrimes will be on the rise

The past two years have seen a rapid shift of everything to online. This led to the rise of global e-commerce that grew by 27.6% in 2020. With this shift, the number of people working from home and using personal devices for work has increased to 67% from only 45% before the pandemic. Thus, security has become important in organizations than ever.  With this shift, cybercrimes have been on the rise. According to reports by the FBI, there were 69% more cybercrime complaints in 2020 compared to 2019. These cases include those caused by distributed denial of service attacks, malware attacks and phishing. This is expected to continue rising. The good thing is that AI is expected to help businesses fight cybercrime by automating some tasks, including DDoS attacks. 

Responsible AI will take center-stage

As AI adoption continues gaining ground, there have been concerns about responsible and ethical uses of AI. According to Gartner, responsible AI helps achieve fairness and avoid biases in data, building trust, transparency and explainability. Although AI has shown a lot of potential, governments and responsible agencies will need to work on the moral implications of this technology. In 2022 and beyond, governments will adopt strategies for ensuring automation outcomes have positive moral implications, and that AI does not discriminate against specific groups of people based on their ethnicity, gender, age or religion. According to Gartner, as we head to 2022, every company could have up to 35 AI projects in development.

National AI strategies are emerging

As more and more countries realize the potential of AI in boosting growth, resilience and productivity across both the public and private sectors, national AI strategies are beginning to gain traction. More than 50 countries, including the US, China and UK, have already developed AI strategies, with others already implemented. For example, in the US, the strategy is focused on fostering AI talent, addressing the semiconductor shortage, fighting cybercrime, and ensuring the country leads in the AI race. Last year, the Biden administration poured billions of dollars into AI projects and initiatives. This is expected to influence AI trends for decades to come.

Artificial intelligence (AI) is undoubtedly one of the revolutionary concepts of the twenty-first century. For this reason, we hear talks about AI and how it can revolutionize almost every industry in the world. While you might have heard how AI will revolutionize manufacturing, learning, and other industries, not much has been said about the positive impact of AI on the hiring process. There has been an increase in the use of AI in employment circles in recruiting and hiring. Here are a few areas that AI is changing in the hiring process.

1. Candidate scanning

A single job post may receive more than 200 resumes, yet only four or even fewer candidates may be selected for the interview. This makes it hard for the recruiters to find the right candidates for the job in a short time. However, recruitment AI can help recruiters screen candidates using an algorithm that finds certain keywords in the resume of a particular candidate. This not only saves time but also enhances efficiency in the recruitment process. Some AI software can also analyze candidates' data sources and online presence to understand their history and abilities. This information is critical in discovering a suitable candidate for the job.

2. Candidate evaluation

AI-powered applications can speed up the interview process by increasing the speed with which potential candidates are evaluated. The pre-screening process can remove the interviewer's biases that may lead to the elimination of the right candidates from the selection. The pre-screening digital interviews can also be shared among hiring teams, allowing the members of the teams to conduct a transparent interview process and allow candidates to participate in remote interviewing.

3. Predictive intelligence

Recommender systems will provide a shortlist of the top-ranked candidates, cutting through the noise. This will minimize the work of sorting and reviewing, which usually takes a lot of time, even in instances where hundreds or even thousands of applications have been made. Predictive intelligence can share additional insights about candidates, including their values, traits, personality and communication skills. Simply, it guides and helps simplify the process of hiring.

4. Minimize the cost of every hire

Automation of candidate screening and interview processes during hiring is a big step in cutting costs. It allows companies to reap big due to a more streamlined and stress-free hiring process. Automation also offers an immediate pay-off in time and enhances efficiency. In frontline customer service and retail roles where there is a recurrent need for staff to fill the positions, automation is the best alternative to quickly conclude the hiring process and help get the best staff to fill specific company needs. Interview automation can enhance a company’s high-volume recruitment process and help make it a more efficient and enjoyable experience for those involved in the process.

5. Targeting and outreach

Using AI in the recruitment process helps businesses and talent scouts avoid mistakes while targeting potential talent for the organization. Scouts can use refined searches to seek candidates with certain attributes through AI. Candidate targeting can help companies increase the diversity of their workforce through the elimination of biases.  Furthermore, AI in the recruitment process can help discover, reach and engage relevant talent and potential employees. Instead of eliminating human touch in the hiring process, AI eliminates the time-consuming process and allows human professionals to focus on finding the right candidates to fill specific posts within an organization.

As an employee looking to upskill in your career or who wants to become more competitive in the job market, AI and machine learning are some of the areas that you might want to look into. Jobs that require AI or machine learning skills are on the rise, and they are expected to increase by 71% in the next five years. With this, people with AI skills will be in high demand in the future.

We live in a world where customers have become increasingly aware of what they want. As such, pressure is mounting on businesses to deliver quality and develop products and services customized to meet the needs of the enlightened customer. Sales and marketing have become complex, and conversion rates are often determined by how you understand your customer. To address these challenges, companies have started adopting artificial intelligence to give them the push they need to thrive. AI in sales allows businesses to use modern technologies to help machines perform cognitive duties like humans or even more efficiently. As a salesperson, various smart technologies in AI can help you improve your performance considerably. These technologies use AI to predict leads and customers that can buy from you.

Role of automation in sales

Generally speaking, sales and marketing have been slow in adopting artificial intelligence and AI. Although there is immense potential in adopting technology to solve problems in sales, it was not until recently that companies and individuals saw the need to adopt it. Large multinationals have already put themselves in a strategic position by capitalizing on this technology to make an impact. JPMorgan Chase, for example, has managed to use AI to increase headline clicks by more than 400%. According to Salesforce, the recent craze in chatbots will see more organizations adopting this technology to enhance customer service and sales. According to Gartner, 70% of customer experiences in the next three years will involve machine learning components in the next three years.

What are the benefits of using AI in your sales efforts?

As a business that wants to grow, AI is fast becoming one of the secrets that allow you to navigate the ever-growing entrepreneurial world. Here is what AI can benefit your business

1. It gives room for the development of business strategy

Artificial intelligence allows you to focus on creating strategies to generate more leads. It offloads monotonous, repetitive tasks that take sales reps' time through machine learning programs that work autonomously. Therefore, the sales reps will have enough time to develop and implement strategies. With the help of AI, you can effortlessly perform tasks such as data consolidation, honing customer profiles, choosing the right content for leads, and sending content leads. With the AI successfully doing this, you get more time to develop strategies that improve the customer experience. This increases the profits for businesses.

2. It increases the leads

Connecting each piece of information is time-consuming. This is also the case when following up the qualifying leads, building them, and sustaining business relationships. However, adopting AI for sales increases the speed of the delivery of results and reduces the time consumed in such duties. Furthermore, AI increases leads because it allows businesses to target and reach specific prospects.

3. Significantly drops costs

Adopting AI automates most activities that salespeople engage in. This will reduce the cost of operations and will cut down many processes, which consumes time and money. With the automation of such activities, you will save money to invest in more significant activities and remain with profit to expand your business.

4. Eases-upselling and cross-selling

With AI, sales reps are better placed to identify the prospects likely to buy their products or services. Salespersons can also use AI to determine when they are likely to make sales and when clients are likely to buy from them. The advanced AI algorithms pick prospects that can sign up for specific products or services from your organizations.

Generally, your sales efforts are likely to benefit significantly from the advancement in AI. It is likely to increase returns and bring other promising deals in sales. Therefore, identify the areas that this technology can help in your sales efforts and invest in them.

Artificial intelligence (AI) has become one of the leading technologies that have positively affected many industries. Among the sectors that have dramatically benefited from technology over the years is the retail sector. The rise of online shopping, whose adoption rose with the pandemic, has provided both the sellers and the buyers with the right atmosphere to embrace technology like never before. Lately, it has become clear that there is no technology to boost online shopping like artificial intelligence. Here is how AI helps make sales more intuitive.

1. Lead generation and scoring

Under normal circumstances, sales representatives take a lot of time trying to sell to leads, most of whom are not interested in their product. These leads will not think of buying the product, no matter how talented or skillful the sales rep is in convincing them. This is a lot of time wasted, and resources lost chasing the wrong people. Without enough data on the leads, sales reps will not differentiate the right potential customers from others, and their efforts will not pay off. They follow the leads and hope for the best. AI software scores leads based on the data obtained. It, therefore, makes the scoring more data-driven and helps sales teams focus on potential buyers. AI also compares the obtained insights with the accounts reports, therefore, making targeting more straightforward.

2. Enhances communication with customers and prospects

While recognizing promising leads is a good thing, it is never enough. Following them and reaching out to both the customers is equally important. AI can help sales teams achieve this by easing communication with customers. For instance, chatbots that use natural language processing enable the collection and processing of information from consumers. While not all chatbots are sophisticated enough, they help collect critical information from customers around the clock, which can be used as feedback to improve service delivery. Chatbots can also be very useful and fast in answering the questions that the customers have at any time, which is something that sales reps cannot manage.

3. Personalized recommendations

New technologies are improving customer experiences. Through data collected from different sources, a good customer experience can be achieved by processing data and using the information to target leads. This makes sales much more straightforward and eases the process of gathering data needed to make decisions. AI-based algorithms generate personalized recommendations based on the data available, with an example being those thrown at us every time we visit e-commerce websites.

An algorithm that takes a snapshot of user browsing history, demographics, interests, and preferences makes it easy for retailers to identify what the customer might be interested in and recommend in advance. This helps customers buy exactly what they want and spend the shortest time possible shopping. AI is sometimes so efficient that as much as 35% of consumers in sites like Amazon get precisely what they want from recommendations and will be willing to pay for it.

4. AI improves the productivity of salespersons

Apart from improving the interaction between sales teams and the customers and the interaction with the rest of the world, AI also enhances the work of sales reps. AI can automate workflow in the entire organization. It can automate task delegation by prioritizing tasks based on different parameters such as objectives and deadlines. AI can also recognize promising leads and assign them to the best sales reps automatically. It can also delegate tasks or even set up meetings based on the availability of sales reps. This saves a company a lot of time and money while also streamlining operations. It reduces noise and chaos in communication and leads to precision in operations.

Source

https://learn.g2.com/ai-for-sales

Like any other breakthrough modern technology, machine learning was once viewed as science fiction. However, with the developments in this area and the applications in the real world today, there is no doubt that machine learning abilities are only limited by our imaginations. Recent innovations and machine learning applications have made tasks efficient, more feasible and precise than ever before. However, this can only happen if the ML is adequately trained with the right data. Here are some things that are happening in machine learning.

1. No-code machine learning

Although most machine learning is set up and handled by computer code, it is no longer the only option. Rather, no-code machine learning, a way of programming machine learning applications without necessarily passing through the long, cumbersome, complicated processes of pre-processing, designing, modelling, design, collection of new data, retraining and deployment. No-code machine learning has some advantages, which include quick implementation, reduced costs and simplicity. Because of its potential to simplify the machine-learning process significantly, it reduces the time that one needs to become an ML expert, making ML applications accessible to developers. This is one of the areas that is fast gaining momentum in the ML landscape.

2. TinyML

In a world where IoT solutions are increasingly being adopted, TinyML is a good addition. Although there are large machine learning applications, their usability is limited. This makes it necessary to have smaller applications. By running small-scale machine learning programs on IoT and edge devices, lower latency, low power consumption and lower bandwidth are achieved. This is particularly good for IoT devices because data does not need to be sent out to data processing centers. This trend is opening up applications in sectors like predictive maintenance, healthcare, and agriculture, where IoT devices are making a difference.

3. AutoML

AutoML aims at making the building of ML applications accessible to developers. Since machine learning has become widely adopted in many industries, there has been an increase in demand for off-shelf solutions. AutoML seeks to bridge the gap by making simple solutions accessible to everyone without relying on ML experts. This simplifies the work of data scientists who have to focus on aspects like preprocessing, features development, modelling and development of neural networks. These are tasks that are complex and require the input of professionals. Since some of the tasks can be complex, AutoML simplifies things through templates. AutoML enhances data labelling through tools and automates the tuning of neural network architectures.

4. Full-stack deep learning

There has been a growing demand for full-stack deep learning in creating libraries and frameworks to help in the automation of tasks. This has led to a rise in new business needs. Full-stack deep learning entails developing deep learning models and connecting them to the external world for users to use them. Engineers have to wrap the deep learning models to infrastructure.

5. Unsupervised ML

Automation is a growing trend in many areas, and data science solutions are also taking this into consideration. This means that human intervention in various areas will be minimized significantly. However, machines cannot learn in a vacuum. Instead, they have to take new information from humans and analyze them. Meaning they need human data scientists to feed the information. However, the unsupervised ML focuses on unlabeled data where it has to draw conclusions on its own without guidance from data scientists. With these capabilities, ML can be useful in studying data structures quickly and identifying patterns. The information is then used to enhance and automate decision-making. Clustering is one of the techniques that can be used to investigate data.

Technology is taking over every sector of the economy. Machine learning is one of the technologies whose adoption is fast gaining traction in all sectors of the economy, from manufacturing to human resources. Education is one of the areas that machine learning is changing. Here are some essential machine learning applications in education.

1. Adaptive learning

Adaptive learning capabilities made possible by advanced machine learning algorithms analyze students' performance in real-time and modify the curriculum and teaching methods based on data. This helps personalize the engagement and makes the curriculum adapt to a student's needs for better learning. The algorithms suggest learning paths that a specific student should follow and books or materials that are good for a specific student.

2. Increasing efficiency

Machine learning can improve content and curriculum than any other method. It helps divide content accordingly by first understanding the potential of every student. This enables analysis and identification of what will work best and what is suited for teachers and students. Machine learning eases the work of teachers and students and makes them happy and comfortable with learning. It also increases the rate of involvement and their love for participation and learning, increasing the efficiency of education.

3. Learning analytics

Teachers often encounter problems while teaching. Most of the problems are associated with the understanding by students and knowing if they understood what they were taught or not. With learning analytics, the teacher can gain insight into data and understand their students better. By going through data and interpreting it, they can make connections and conclusions that impact the learning and teaching process. Learning analytics can also suggest paths that a student can follow and the benefits of doing so.

4. Predictive analytics

Predictive analytics entails knowing the mindset and needs of students. With machine learning, teachers can make conclusions on the things that are likely to occur in the future and design a syllabus to accommodate them. With the learning and tests that students take, the teachers can easily predict students' performance in the final exams and the students that will excel. Such information helps the faculty and parents take appropriate measures to help students improve in their weak areas and perform well.

5. Personalizing learning

This is perhaps one of the leading advantages of machine learning in education. With machine learning, the learning patterns of students can be understood and their requirements taken into account. Through this method, students can learn on their own and at their pace. They can also decide what to learn and what not to learn. Students can choose the subjects they are interested in and the curriculum or pattern they want to follow.

6. Evaluating assessment

Machine learning in the form of AI is used to mark exams and grade students accurately and faster than humans can do. This is a solution to the OMR answer sheets, which was the only technology available in schools to mark the exams. Apart from being faster, machine learning does not require too much human intervention and is reliable because of low chances of error.

With the increasing awareness of technology in education, changes are visible for everyone to see. Although the use of technology in education is still in its early stages, educators realize that machine learning can revolutionize the education field for the better. Apart from the above aspects, machine learning can also enable automation of tedious, time-consuming, and costly tasks. As e-learning continues to gain traction, the number of machine learning applications is likely to rise, which is likely to alter the education landscape.

From small to large enterprises, everyone in the business world wants to jump on the machine learning bandwagon. This can be seen from the Gartner 2019 CIO survey, which found that 37% of organizations have implemented some form of artificial intelligence (AI) or machine learning (ML) in their operations. This represents a significant shift in the business landscape as organizations seek to make their service provision better. While many people may think only the big brands are implementing these technologies, do not be fooled that AI and ML are not good for small businesses. Small businesses are set to benefit more from AI and ML than bigger ones. Here are some ways that ML is helping small businesses.

1. Improving hiring decisions

The success of any small business depends on the hiring decisions that it takes. In most cases, this process is not easy because the traditional methods do not make talent discovery any better. However, with the right technology in place, hiring and retaining employees are enhanced. This is even better for smaller companies that have no formal human resource department or where a solo practitioner operates everything. The human resource technology that has evolved to include AI and ML reduces repetitive tasks and automates HR functions that were manual.

AI-based applicant tracking systems help small business owners quickly manage the recruitment process by searching resumes that match the job descriptions, scheduling interviews, and contacting applicants. These systems also minimize bias in recruitment, allowing the organization to hire only qualified individuals for the job. The employer can also take advantage of ML-powered retention solutions to keep the high-performing talent and make them happy and engaged.

2. Improving marketing

If you are running a small business, there is a high chance that you have a small marketing department or none at all. Regardless of how big or small your marketing team is, you can still use ML and AI-enabled online marketing solutions to reach consumers. These solutions allow you to personalize services or products, identify the relevant customers and deliver personalized experiences. Small businesses can also improve their email marketing and advertising through machine learning. For smaller businesses, ML allows content personalization and predictive lead scoring.

3. Improving customer experiences

With machine learning, it will not be necessary to increase headcount to serve the increasing number of customers. It becomes easy for your website to solve customer problems and answer questions faster. With services like answer bots, your support can answer customer questions instantly without necessarily engaging human personnel. This leaves the small customer service team free to focus on other important and complex matters.

4. Improving decision-making process

Small businesses can benefit significantly in their decision-making endeavors with the help of AI and ML. Small companies can use machine learning to improve their decision in various areas and processes. For instance, they can use this technology to analyze supply chain efficiency, enhance warehouse management, enable smarter shipping, and improve inventory management. ML can also help reduce risk by collecting data regarding processes and monitor human error to create safe procedures. With AI scheduling assistants, small businesses can also save time on booking meetings.

From the above contributions of ML to businesses, it is clear that this technology is the right one for small businesses. However, although it can help small businesses solve various problems, it also has various challenges that need to be solved. Some of the problems include data, technology, and people. Regardless of this, ML presents room for small businesses to minimize the cost of running their operations and enhances efficiency if well deployed.

If you have ever completed a data science project, then you probably realized at some point that achieving even 80% accuracy is not that bad. However, in the real world, such a thing is not allowed and will not cut it. As such, there is always the urge to improve accuracy in algorithms and the results through different checks and actions that offer the right ways to enhance machine learning performance that will lead to better quality. Here are some techniques to make your machine learning efforts effective.

1. Study the learning curves

The first step to improving the results of your machine learning algorithms should begin with determining the problems that your model has. This can be attained by verifying the learning curves against a test set while varying the training instances. With this, you immediately find out if there is a difference between the in-sample and out-sample errors. If you find errors that are both high and similar, that will be a sign that you are working with a biased model.

2. Use cross-validation (CV) correctly

A large difference between the CV estimates and the result is a massive problem that appears with a test set of fresh data. This problem means that something has gone wrong with cross-validation. Although cross-validation is good in prediction performance, this issue means that there is a misleading indicator, which causes incorrectness and unsatisfactory results.

3. Handle the missing values

One of the biggest challenges in machine learning models is missing values and how people handle them. While this may not necessarily be their fault, depending on the material on the web, which advocates for mean manipulation and replacement of null values with the feature’s mean as a way of handling the missing values are not entirely correct. The first question that needs to be asked in such incidences is why the data is missing in the first place. This should be followed by considering other approaches to handling the missing data instead of using mean/median. Some of these methods are feature prediction modeling, K Nearest Neighbor imputation (KNN), or deleting the row, although this method is not recommended at all times.

4. Apply feature engineering

There is a possibility that bias may still affect your model even after trying the above methods. If this is the case, you should try to improve the performance of your model. This can improve the target response. This can be achieved using the polynomial expansion or the support vector machine class of algorithms. The former can automatically look for the better feature spaces in a manner that is memory optimal and fast computationally. While these methods are handy, the human expertise and understanding of the method needed to solve the data issue that the algorithm is trying to learn cannot be substituted. Therefore, features are created based on your knowledge and ideas of how things work in the real world. Therefore, while machines have improved significantly, humans are still unbeatable in some areas.

5. Look for more data

After exploring all the previous options, there may still be some issues and high variance that needs to be dealt with appropriately. In such a case, the only option is to increase the size of the training data. Doing this could mean you have increased new cases or new features. Adding more cases requires you to carefully look into the data and determine if you have similar data at hand. A great way to add new features is to locate an open-source data source and match the data with your entry series. You can also obtain both new cases and features through data scrapping from the web.