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 (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.

According to LA Weekly, artificial intelligence has discovered 17 psychedelic compounds. 

Artificial intelligence and psychedelics are two of the most rapidly developing spaces on the globe. We sat down with April19 founder Dr. Suran Goonatilake to talk about the merging of the two.

Read the article LA Weekly

According to Homeland Security, AI will soon be able to help differentiate between wildfire smoke and fog.

During extremely dry, hot, and windy weather, being able to differentiate wildfire smoke from fog and other false indicators is invaluable to analysts in PG&E Wildfire Safety Operations Center and fire agencies.

Read the article Homeland Security

According to ZD Net, low code applications could allow more AI to be built.

How high will low code applications go? The jury is still out on how high this is all going. Low code and no code may even play a role in enabling business users to build artificial intelligence-driven applications, some observers predict

Read the article ZD Net

According to MyCBS4, Gainesville Florida is working to become the global center for artificial intelligence development. 

Gainesville is making waves in the realm of artificial intelligence.

Read the article MyCBS4

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

When it comes to the advancement of technology, artificial intelligence (AI) is one of the technologies often discussed in tech circles. The recent advances in neural networks have made this technology exciting and have presented new opportunities for future growth. At the same time, it has increased areas where AI can be applied. While this advancement in neural networks is a good thing for AI, planning is also an area that must always be considered to deal with the limitations of this technology.

AI planning involves teaching machines how to plan ahead. The process entails specifying problems informally using natural language, which is difficult for the planning system to understand. This is unlike the traditional approaches where the specification of the goal or problem to the planning system is assumed. Improving AI’s ability to understand problems through planning is an ambitious journey to make this technology part of everyone’s journey. For instance, proper planning will lead to the creation of systems that understand things such as our daily routines.

AI learning and planning

Human intelligence is defined by both learning and reasoning. While learning allows humans to take advantage of everyday activities, planning allows humans to plan future actions to achieve a specific goal. Therefore, planning complements learning and is one way of exploring the future, while learning involves getting knowledge from past experiences. Like humans, AI may encounter problems that are not related to past experiences and therefore require new solutions. Such problems are often difficult to solve this will require learning. Learning is used to make AI planning more efficient. This is just one way of ensuring planning and learning work together. Learning leads to the generation of macro-operators that increase the speed of the problem-solving process. For artificial intelligence, learning enables tuning of internal planning heuristics. This is critical in completing the definition of domain knowledge, which speeds up planning like hierarchical task networks.

Human vs. AI

Human intelligence is the point of reference when comparing the capabilities and the potential of AI. Although human intelligence is not necessarily the ground truth and AI does not entirely mirror human intelligence, the comparison allows us to get valuable insights about the parts of AI that are more developed and those that need more attention and improvement in the future. In the sub-area of learning, for example, the view out there is that AI is fundamentally different from human learning.

Unlike humans who learn from very few examples, AI requires many examples to learn. Humans have good generalization capabilities from just a few examples. On the contrary, AI requires many examples, which can be attained by availing large data portions. AI is said to be greedy, shallow, and brittle because of the vast amounts of data it requires to learn. It is shallow because neural networks have a narrow knowledge of commonsense and brittle because it has a limited generalization power. Understanding these differences between humans and machines helps define the objectives of AI and what it needs to achieve.

Unlike machines, humans have an excellent way of formulating planning problems. Humans know how to choose the details and the right abstraction levels around us and what to ignore at a specific level of abstraction. This is something that is not always easy for machines. Therefore, humans have an advantage in recognizing a problem and expressing it.

Conclusively, planning does not always require training or even the availability of vast amounts of data. Instead, it requires a combination of planning and learning. However, the goal and the size of the input can change the problem instance to another. The bottom line is understanding what and how important a problem is. That is intelligence.

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

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.

Artificial intelligence (AI) is considered one of the greatest innovations since the internet. With the promise that this technology has shown, it is now almost everywhere, and you are possibly using it in one way or another without even knowing. Machine learning (ML) is one of the key applications of AI that has become useful in various fields such as computing, software development and websites. Here are some of the key uses of machine learning that you might have used at some point, perhaps with no idea about it.

1. Virtual personal assistants

Virtual personal assistants such as Alexa, Siri and Google now have become popular in many households. As the name suggests, they help in completing specific tasks and finding information when commanded over voice. By simply asking these assistants questions like “What is my schedule”, you get a response from the virtual PA, which first looks for the information on the query asked. You can even command it to play music, set the alarm or remind you of a particular event, and it will do that appropriately. Machine learning is a critical component in virtual personal assistants. It helps in the collection and refining of information based on your history. It also allows VPAs to render results that are tailored to the user’s preferences.

2. Filtering email malware or spam

Email spam and malware are key problems for many people. They often result in the loss of crucial information and money, which many malicious individuals look for. Machine learning, however, offers a new glimmer of hope in combating malicious emails. It filters emails by analyzing words, using case base and thorough extraction of words from images, that cannot be attained using the traditional rule-based methods. AI-based spam and malware filtering algorithms are accurate and keep changing to adapt to the changing behaviors.

3. Chatbots and online customer support

The majority of websites nowadays allows visitors to chat with customer support representatives. While some of those you chat with are real human customer representatives, most of them are chatbots that assess the questions you ask and answer them accordingly.  These bots extract information from the website you visit and present it to you. Using machine learning, the bots processes the questions asked and extracts relevant answers. The use of AI-based chatbots allows companies to help their customers at all the time without increasing the cost of operations by hiring human customer care representatives.

4. Refining search engine results

Search engines such as Google, among others, rely on modern technologies to improve the search results for their visitors. Every time you hit “search” after typing some keywords, AI-based algorithms at the backend saves your searches and how you respond to the results displayed. If you select or open the results and stay on a specific page for a long time, the search engine will assume that the research they displayed were in accordance with your desires. The opposite will be the case if you keep moving through pages without opening the results. In such cases, the algorithms at the backend will update themselves using machine learning to serve content better.

5. Product recommendations

If you have tried looking for products online via online stores, then you have definitely interacted with machine learning at one point. At one point, you might have received an email with recommendations on products that you have looked for at any given time. These recommendations make your experience better. This is all machine learning making things work better for you.  Machine learning also makes it easy to search websites by giving you recommendations on the first page.