The past two years have been among the most challenging ever witnessed in the history of humanity. They were full of untold suffering among citizens, businesses and governments across the globe. With the spread of the pandemic, which resulted in far-reaching health and safety restrictions, online work became the norm. This is where modern technology came into aid. One modern technology that has proven helpful during the pandemic is artificial intelligence (AI). AI applications played a critical role in saving lives and fostering economic resilience during difficult moments. With the potential of AI in different areas, this technology is advancing on many fronts with research and development to improve its capabilities of this technology. AI is now driving other industries, including autonomous driving, e-commerce and quantum computing. Here are some AI developments that will shape 2022 and beyond.

1. AI in vaccine development

Developing a vaccine is a complex process that usually takes years or decades to achieve. However, with modern technologies, it is easy to sift through millions of materials and get the information needed to develop vaccines. An example of the usefulness of AI was witnessed during the COVID-19 crisis, where a record speed of vaccine development was achieved. AI models allowed researchers to analyze vast amounts of data about coronaviruses of the past and how they work.

2. Automated driving and robotaxis

Autonomous driving has been gaining ground over the past decade. In 2022, it has continued to be one of the areas that major companies such as Baidu and Uber are focusing on. These two are currently testing driverless cars and opening more robotaxi services to the public in different cities worldwide. Fully automated driving enables taxis to drive safely without human intervention, which will be necessary for the scalability and commercialization of autonomous vehicles. Over the years, companies like Baidu have invested heavily in driverless vehicles and launched some of them, including Apollo Go Robotaxi service, which operates in the cities of Beijing, Cangzhou and Changsha.

3. Natural language processing

The natural language processing systems have become more advanced over the past two years. NLP is the ability of a computer system to understand the meaning of text or speech. This ability has revolutionized the way humans interact with machines. This is evident from the widespread use of AI assistants like Siri, Alexa and Cortana. The natural language processing systems understand what people say and act or respond accordingly. NLP has many to offer than just communication. For instance, it can help scale business operations in an organization.

4. Quantum computing

Quantum computing is a new area of competition among organizations and countries. At the beginning of this decade, quantum computing has made some crucial achievements of quantum supremacy. Quantum computing has so much significance for AI since it has the potential to supercharge AI applications compared to binary-based traditional computers. For example, quantum computing can be used to run a generative machine learning model via a generative dataset, which a classical computer will take too much time to do. The use of quantum computing increases accuracy in the real world. On the other hand, advanced technologies like deep learning algorithms also play a critical role in the development of quantum computing. Deep learning algorithms also play a critical role in quantum computing development.

5. AI chips

With the proliferation of AI, AI hardware has continued to develop in 2022. There has been a launch of several AI chips customized to perform specialized operations. Although ordinary processors can support AI tasks, AI-specific processors are modified to do tasks like deep learning. As AI applications increase, there is a need for performance or reduction of cost. This can unlock value for companies with a network of data centers meant for cloud services.

There is a major disconnect between what people would want to do with AI and their potential to actually do it. With the rising number of companies now adopting artificial intelligence in their operations or planning to invest in it to enhance their products, operations, and services, this technology must be understood with regard to its potential and challenges. Get past these roadblocks to AI implementation and succeed in your implementation plan.

1. Data issues

This is one of the leading roadblocks in the journey to implementing AI. It is largely caused by a lack of relevant and insightful data free from bias and does not compromise privacy. However, the availability of massive volumes of data does not guarantee the availability of useful data. A McKinsey survey identified that out of 100 companies that have piloted AI, 24 of them stated that the biggest impediment to proper AI implementation is the unavailability of useful and relevant data. While organizations continuously collect data that should be sufficient when fed into AI systems, most of it goes to waste. From this, it is evident that collecting data is not the end of the process. Rather, there is a need to collect useful data.

2. High cost of tools and development

When planning to develop and implement AI systems in an organization from scratch, the cost of acquiring human resources, technology and tools can be high. This cost is high mostly when a company is starting and has no staff with the right technical skills or tools. For organizations with small budgets and lack of experience that cause the development of AI to be outsourced, the challenge is similar. However, an alternative to this will be to leverage tools developed to work with systems that companies in a given industry are already using.

Lack of a clear leadership

When it comes to big organizations investing in AI implementation, leadership is a critical component without which things will not work as expected. However, larger organizations do not have a problem when it comes to leadership. In fact, these organizations often have many leaders with diverse opinions. While opinions are good for progress, too much of it can turn into a problem, even with a lack of leadership. While most leaders in organizations think about investing in emerging AI technologies, they often fail in their duties because of a lack of the same vision across the organization. For example, the IT department might have enthusiasm about the implementation of AI and how it will turn the organization's fortunes. On the other hand, the other department might be too slow to cope with this pace of change. This unevenness might result in poor adoption of technology, which can increase costs over time and may lead to conflicts in the overall AI adoption strategy.

Shortage of skills

Even after the organization decides to implement AI, most of them lack adequate skilled personnel to develop AI systems. A survey by Deloitte indicates that 31% of companies see the skill gap as a key challenge in the adoption of AI. The key challenge here is that most companies cannot conduct the adoption on their own due to a lack of required skills. There is also a mismatch of skills because some skills are more available than others. It is important to note that the implementation of AI requires a broad set of skills. This problem can be addressed by outsourcing the development of AI systems to other organizations that already have existing tools. This will enable the organization to fix the labour problems that might hinder the implementation of AI.

AI and blockchain have proven to be quite a powerful combination that will improve almost every industry. These two technologies can offer exciting opportunities individually. However, when brought together, they may offer even more than anticipated. By combining the AI's predictive power and the robustness and security of blockchain, companies can develop smarter, safer, transparent and cost-efficient business automation systems. Blockchain and artificial intelligence are working together to upgrade all industries from transport and logistics, healthcare, financial security and food supply.

Integrating AI and blockchain offers better security against cyberattacks while building better transparency. Furthermore, AI can enable organizations to sift through massive datasets and create new scenarios and patterns based on the behaviour of data. On the other hand, blockchain helps remove errors and fraudulent data sets. In the end, the new classifiers and patterns created by AI can be verified using decentralized blockchain infrastructure, which can also be used to verify the authenticity of data. This can be useful in consumer-facing businesses like retail transactions. Data gathered from the customers through blockchain infrastructure can create marketing automation via AI.

How AI can add to the blockchain

Security

With Artificial intelligence, blockchain technology will become much safer and secure for proper future deployments. AI algorithms will become increasingly useful in blocking fraudulent transactions in the financial industry, which increases the security of these transactions. Currently, experts are building algorithms that will enable AI to work on encrypted data or information without exposing it. Furthermore, machine learning will help deploy blockchain apps and predict possible breaches.

Efficiency

AI can help optimize calculations in the blockchain's miner load. This results in less network latency and increases the speed of transactions. It also reduces the carbon footprint of blockchain technology because energy spent would be less if AI machines replaced miners' work.

Trust

Blockchain, in conjunction with AI, means that users will have clear records that conform to the system's thinking process. AI helps bots trust each other and increases machine-to-machine interaction. This allows bots to share data and coordinate decisions.

Improved management

Although human experts get better at cracking codes over time, machine learning-powered mining formula can eliminate the need for the human experience because it can learn how to crack codes if the correct data is fed with the right coaching knowledge. Furthermore, AI helps manage blockchain systems better than humans and can learn this with time if the correct data is provided. This means that management can be improved in time without necessarily investing a lot in human experts.

Automation

Artificial intelligence, automation and blockchain can bring new value to the business processes and removes friction. It adds speed and increases efficiency. For example, AI models embedded in smart contracts and executed on a blockchain can recommend expired products to be recalled, transactions to be executed, and other actions.

Storage

Blockchain is great for storing highly sensitive and personal data. When such data is processed with AI, it can add value and convenience. One such example is healthcare systems that make accurate diagnoses based on medical scans and records.

Accessing and managing the data market

Since distributed ledger can store vast amounts of encrypted data and AI can manage it effectively, new use cases of AI and blockchain emerge. Users can store their personal data in the blockchain and sell access to the same data. Consequently, data, model and AI marketplaces emerge. Big players like Amazon, Google and Facebook have access to large data volumes and AI processes can be beneficial to them. Smaller companies can access the same pool of information and even compete with tech giants from the marketplace.

Edge computing has attracted a lot of interest and new use cases, especially with the rising adoption of 5G. According to the report by the Linux Foundation titled The 2021 State of the Edge report, the global market cap of edge computing infrastructure is expected to be worth more than $800 billion by 2028. Companies are also investing heavily in artificial intelligence, with a McKinsey survey of 2021 showing that 50% of the respondents have already implemented AI in at least one business function. Here are the developments in edge AI and how it will impact modern enterprises.

What is edge AI?

Edge AI is a technology that allows faster computing and insights, enhanced security and efficient control over the continuous operation. It is the deployment of Artificial Intelligence (AI) in devices in the physical world. It is called edge AI because the computation is carried out at the network's edge. This is close to where data is located.

How edge AI will transform enterprises

Efficient adoption of Edge AI models and infrastructure can positively affect businesses. For instance, edge computing can allow the handling of bulk workloads on edge and near the edge, which is one way of improving performance, efficiency and scalability in the use of data. Because of this, many global businesses have already adopted and are reaping big from this technology. With edge AI, companies can efficiently perform tasks like monitoring the production of an assembly line and driving autonomous cars, among other applications in different industries. Furthermore, the recent 5G rollout in most countries is proving to be a timely boost for edge AI. This has increased the applications for technology across industries. Some key benefits of edge computing in collaboration with AI to organizations include efficiency in predictive maintenance and asset management, reduction of field issues, better customer satisfaction and faster inspection span. Implementing edge AI is a good decision for a business, with reports indicating that there is an average 5.7% return on investment from the industrial edge over three years.

Advantages of machine learning on edge

Machine learning (ML) is a subset of AI that simulates the human learning process using data and algorithms. With the aid of edge, ML can help businesses that rely on IoT devices. Some advantages of machine learning on edge computing include:

Privacy

Information and data have turned into the most valuable asset in the modern age. As such, consumers are cautious of where their data is located. With AI-enabled personalized features in applications, companies can allow users to understand how their data is being collected and kept. This is crucial for brand loyalty.

Reduced latency

The majority of data processes carried out on the network are affected by latency. However, edge AI eliminates the need to send massive data amounts across the network because processing is done at the edge. This minimizes latency and improves user experience significantly.

Minimizes bandwidth

IoT devices within a network process massive amounts of data and transmit it to the cloud. They have to transmit huge amounts of data to the cloud and then analyze and retransmit the analytics to the device. Without adequate bandwidth and enough cloud storage, it would be an impossible task. There is also a possibility of exposing sensitive information during the process. With edge AI, the cloudlet technology, which is a small-scale cloud storage, is enabled at the edge of the network. This enhances mobility while reducing the load of transmission of data. It also brings down the cost of data services while enhancing its flow speed and reliability.

Low cost of digital infrastructure

A report by Amazon indicates that up to 90% of digital infrastructure costs originate from inference which is a crucial process of generating data in ML. However, with edge AI, it is possible to eliminate exorbitant expenses incurred on AI and ML processes that are done on cloud-based data centers. 

Artificial intelligence will use a dataset of hundreds of whale satellite images for research, reports Phys.org.

A new dataset featuring hundreds of satellite images of whales has been published to support the development of artificial intelligence systems which will aid crucial conservation work.

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There are ways to fix artificial intelligence’s ethical problems, reports Forbes.

AI violates privacy. AI-generated output can’t be explained. AI is biased. This is all true, and is happening today, and there’s a risk of these issues accelerating as AI adoption grows.

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According to Business Insider, an engineer working on Google’s artificial intelligence chatbot has claimed it is sentient.

An engineer at Google said he was placed on leave Monday after claiming an artificial intelligence chatbot had become sentient.

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AI and satellites can solve world problems if given a chance, reports Wired.

For the past three decades, geologist Carlos Souza has worked at the Brazil-based nonprofit Imazon, exploring ways he and the teams he coordinates can use applied science to protect the Amazon rainforest.

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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

Machine learning (ML) could not have come at a better time than now. As businesses move towards a new era of digitization, ML is needed to help in various operations of organizations, starting with data processing to customer support. Machine learning algorithms learn from data and find different hidden patterns and insights, which is crucial for businesses to make critical decisions. ML and artificial intelligence tools and algorithms have gained fame over the past few years in business circles and the analytics community due to their potential to help sift through massive amounts of data from different sources. With growing data volumes, higher and faster computational power and affordable data storage, machine learning has become more feasible now than ever.

Here are some ways that your business can benefit from machine learning.

1. Prediction of Customer lifetime value

Prediction of customer value is one of the challenges that marketers face today. This is the same case for customer segmentation. Companies get massive data and access to huge volumes from different sources. This data can be used to derive meaning and gain insights through the identification of patterns.ML and data mining are critical in aiding businesses in predicting the customer's behaviour and purchasing patterns. They also help send offers to specific customers based on their browsing and purchase history.

2. Predicting maintenance

Manufacturing, transport, and logistics companies always focus on preventive and corrective maintenance practices. With no automation, these practices are expensive and mostly inefficient. However, with the emergence of machine learning, companies can now discover patterns and the possibility of machines breaking up. This is known as predictive maintenance. It helps reduce the risks associated with unexpected machine failures and eliminates unnecessary machine expenses. 

3. Eliminates manual entry of data

With machine learning, you can sift through massive amounts of data in a short time and remove duplicates and inaccurate data. Data duplication and inaccuracy are some of the biggest problems that most businesses today due to massive amounts of data coming from different sources. However, predictive modelling algorithms and machine learning minimizes errors and eliminates errors caused by manual data entry. Machine learning improves processes by using the discovered data and allowing employees to use their time to do other tasks.

4. Spam detection

In the age of technology, cybercrime has gone up more than ever. However, machine learning has shown some potential in helping avert challenges emerging from spam attacks. Previously, email service providers used rule-based techniques to discover spam and filter them out. However, pre-existing rule-based techniques are not efficient in detecting spam and phishing messages. With the rise of machine learning algorithms that can adjust based on the data presented, fighting spam has been simplified. 

5. Product recommendations

If you have visited any e-commerce website, you must have seen that products are recommended based on your previous search. This is the work of machine learning. Most e-commerce websites today use machine learning to recommend products to their customers. Unsupervised learning helps develop product-based recommendation systems. These algorithms use purchase history and match it with large product inventory to identify hidden patterns and group products together. These products are then suggested to customers to motivate customer product purchases.

6. Financial analysis

With large volumes of data coming from various sources, machine learning can be used in financial analysis. ML is used by banking and finance institutions for portfolio management, algorithmic trading, detection of fraud and underwriting. Chatbots and other security interfaces are also making their way into these institutions.

As machine learning continues to grow in terms of its abilities, it is clear that more applications will emerge along the way. As a business, take advantage of this technology to boost your productivity and effectiveness.

When it comes to procurement, machine learning is one of the most promising technologies to improve operations. A data analysis machine learning is the application of self-learning automated statistics to solve defined challenges or improve efficiency. Unlike robotic process automation, machine learning can improve its abilities over time. This technology can mirror human decision-making abilities and enable businesses to leverage it in automating tasks and decisions. In procurement, organizations can use machine learning to save time that could have been wasted on tedious tasks and provide insights through the analysis of data sets. Here are some roles that machine learning can perform in procurement.

1. Spend classifications

Spend classification is the process of grouping related spending data into categories using a given approach. This process is important in the preparation of data to spend analyses. The process of classifying data starts with gathering ERP data from contracts, purchase orders, and internal and external data and putting related items together. Spend classification is a tedious and repetitive process which may strain an organization in terms of human resources if done manually. However, with the help of machine learning, it is possible to gather data from different sources and classify it faster and accurately. The procurement staff can be left to do other useful things.  

2. Analyzing stock in transit

Companies that send and receive products must follow the status of materials and products in transit in near real-time. This will help the address emerging problems such as delays and other issues before they can occur. Predictive analytics give them exactly that. It allows them to ensure orders are delivered in time and at the right state.

3. Automated sourcing

Through an increment of supply assignment sourcing, procurement departments can reduce the need for interacting manually while securing materials at the best price, faster and in high quality. With machine learning, the system automatically creates a bidding event when an internal source of supply is exhausted. It will act like a human purchaser, sending the bidding invitation to a specific list of suppliers.

4. Making purchase recommendations

Machine learning can be helpful in making quick purchasing decisions. It helps create systems that act like search engines where customers can search products. With these search engine-lie abilities, the system can be refined to show a list of suppliers that meet specific criteria. A recommendation system can be built to support quick and accurate purchase decisions. A recommendation system recommends the right supplier for a company to source products based on the prevailing market conditions.

5. Matching Capabilities

Machine learning can help in capabilities matching. Since all buyers want their suppliers to fully meet their needs while delivering exceptional service, having the right systems in place to match capabilities of each supplier will be crucial for operations. However, it can be tough to determine the potential of suppliers based on the manual systems. Machine learning can scan the industry for new competencies and align them with business requirements. With this information, the abilities of the existing and new partnerships can be tested and understood. 

6. Tracking and monitoring efficiencies

Efficiencies of operations is what every company seeks to achieve. However, this cannot be possible with manual systems. Machine learning allows organizations to track and monitor the efficiency of every company within the supply chain and rate them based on their abilities and performance. With this information, procurements are made efficient and fast. Furthermore, having information of every organization ensures operations run at their peak in terms of standards.

Generally, as technology continues evolving, we should expect more value and increasing efficiency in machine learning. As it is evident today, procurement companies can no longer ignore machine learning. It is upon them to work harder to benefit themselves with it.

Machine learning is indeed breaking barriers in almost every industry today. In the manufacturing sector, machine learning applications, platforms, and algorithms help find new models, fine-tune the quality of products and optimize manufacturing operations. It enables manufacturers to grow the quality of the product while taking short lead-time production runs. With the advancements in machine learning technology, manufacturers are deploying it to improve the end-to-end performance of their operations and end find solutions to various problems they face.

Here are some ways that machine learning is changing the manufacturing sector.

1. Makes forecasting accurate

One of the biggest and most important areas in the manufacturing sector is forecasting. It is crucial for companies to identify the number of products that would be needed in a specific period. Manufacturing too many products or purchasing many raw materials can lead to extra costs. On the other hand, if a company purchases too little inventory, the chances of failing to meet the customer's demands reduces. This means lost revenue and damaged company reputation. With forecasting made possible by ML systems, companies can start manufacturing the needed products. This will reduce wastage and shortages.

2. Enables optimization of workflows

The majority of people assume that manufacturing has become efficient with time. This is not true because manufacturing capacity utilization has gone down over the years despite various improvements in other aspects of manufacturing. Industrial ML can help optimize workflows and eventually improve the areas that are inefficient. ML solutions allow manufacturers to create digital twins and model various scenarios in these areas. The results of modelling allow manufacturers to determine the best course of action.

3. Reduction of supply chain errors

According to McKinsey, machine learning will reduce errors in supply chain forecasting by over 50% and minimize the lost sales as a result of bad forecasting by 65%. It will enhance product availability and minimize the costs associated with transport, warehousing and administration of supply chains by 5 to 10% and 25 to 40%, respectively. ML will lead to overall inventory reductions of 20- 50%.

4. Predicting maintenance needs

One of the most promising areas that ML can be used in manufacturing is predictive analytics. ML algorithms can predict the time that an equipment may fail by analyzing performance data gathered by sensors in machines. The predictions can allow manufacturers to adopt an optimal approach to repair machines and avoid machine downtimes caused by unscheduled maintenance. Corrective maintenance is cheaper than fixing breakdowns and downtimes. Correct prediction through ML allows preventive maintenance to prevent machines from breaking down.

5. Enhancing quality control

Quality control is an important part of any manufacturing operation. Therefore, spotting problems before the products are shipped to the customer minimizes costs and enhances customer satisfaction through quality. Unlike manual quality control which is flawed, ML-based quality control systems are accurate and can compare products to ideal models based on data-based approaches. According to studies, ML-based quality inspections increase defect detection rates by over 90% compared to humans.

6. Enables more product personalization

Over the recent years, customers have begun to demand more personalized products, and manufacturers have no option but to comply. Consumers' reliance on digital services has led them to expect personalized products and services. This can be a big challenge to manufacturers. However, machine learning can help them meet these demands and increase satisfaction and loyalty. With ML, manufacturers can develop hyper-personalized products for their customers and increase sales. Machine learning models can infer features that customers did not mention but would love if added. With the advancement of machine learning models, there will be an increase in customization, and companies will benefit from it.

Artificial Intelligence (AI) and Machine Learning (ML) are some of the closely linked terms in the field of computer science. They are among the most trending technologies used when talking about intelligent systems and big data. These terms are often confusing, and most people interchangeably use them.

Artificial intelligence

Artificial intelligence (AI) is a technology that allows machines to simulate human behaviour. It is a subset of AI that allows machines to learn from past data without or with little human intervention. It intends to make a smart computer system like humans that can solve complex problems. AI systems do not require to be pre-programmed. Rather, they use algorithms that can use their intelligence to learn. Some algorithms include machine learning algorithms like reinforcement learning and deep neural networks (DNN). AI is currently used in various places and platforms like Google, Siri, websites, smartphones, and AlphaGo.

AI can be classified into three main types based on their capabilities. They can be:

• Strong AI
• Weak AI
• General AI

Currently, there are weak and general AI. However, the world is heading towards Strong AI, which is expected to be more intelligent than humans. AI systems do not require to be pre-programmed. Rather, they use algorithms that can work through their own intelligence. The machine learning algorithms like reinforcement learning and deep learning enable such learning.

Machine learning

Machine learning entails extracting knowledge from collected data. It is another subfield of AI that enables machines to learn from the historical data or without necessarily being programmed explicitly. It enables computer systems to make predictions or decisions using past data. It uses vast amounts of structured and semi-structured data to enable machine learning models to generate accurate results or offer predictions based on that data.

Machine learning consists of algorithms that work on their own. However, it uses specific domains in its work. Currently, machine learning is used by giving machines access to data and letting them learn by themselves. This is called the training of models. ML is used in various areas such as online recommender systems, mainly used by e-commerce companies, spam filtering in emails, Facebook Auto-tagging suggestion systems and Google search algorithms. Machine learning can be divided into three types:

• Supervised learning
• Unsupervised learning
• Reinforcement learning

Unlike AI, which prioritizes making intelligent systems perform like humans, machine learning teaches machines through data how to carry out certain tasks. The tasks they carry out are the specific ones to which they are trained to perform. On the other hand, it is concerned with accuracy and patterns while performing these tasks. Machine learning enables computer systems to make predictions or make decisions using semi-structured and structured data to generate accurate results or make predictions based on this data. For example, machine learning systems can read a text and determine what the person who wrote it is saying. Furthermore, they can also listen to music and decide if it can make a person happy or sad or work out the possible mood that the said music can trigger. Machine learning algorithms can also compose music and express the themes that the listeners can appreciate.

Some key similarities between AI and ML are that they both include learning and self-correction. Furthermore, they both deal with semi-structured and structured data. However, unlike machine learning, artificial intelligence, on top of self-correction and learning, also includes reasoning when introduced with new data. Furthermore, while machine learning deals with structured and semi-structured data, AI deals with structured, semi-structured and unstructured data. This is a critical difference between the two closely related technologies.