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. 

New technologies like artificial intelligence (AI), machine learning (ML) and the Internet of Things (IoT) are changing different industries for the better. AI, for example, is altering the creative landscape, and industries like music, fine arts, science and architecture are getting the best out of it. Computational creativity, a study of building software with behaviour deemed creative, has become one of the fast-growing areas of technology over the past two decades. This software can be used for creative tasks like writing poems, composing music or painting pictures. 

Composing music

AI has been proven to have an important place in music. Until recently, most AI efforts had been on composition and improvisation systems. Little effort had been devoted to performance since the 1950s. However, in1958, Hiller and Isaacson pioneered the ILLIAC computer, which is best known as a music computer. The Illiac Suite composed song following the "generate and test" problem-solving method. It generated notes randomly using Markov chains.

As years went by, the composition of music and AI systems used to do so have advanced significantly. With the rise of social media, artists are now using AI to make music. AI is reflecting the changing nature of art in the world. AI and social media have influenced the expansion of art. From the artists' perspective, the internet gives them the ability to produce work that can reach many viewers and connect better. AI, which is essentially a pattern-recognition system, finds patterns in data fed into it and makes decisions based on these patterns.

A good example is the Schubert symphony, where AI determined the musical notes and where they should be placed. As AI matures, its potential in the creative industry also grows. This can be seen in its ability in the music industry, where AI is beginning to create, perform and monetize social compositions.

Synthesizing Expressive Music

One key limitation of computer-generated music has been the lack of expressiveness (gesture). This is known as the nuances of performances, which can be unique and interpretive. However, this has been one of the areas studied since the 1990s when Johnson developed an expert system to determine tempo and articulation that needs to be applied. Past studies have shown that AI can deal with expressive parameters of music like articulation, dynamics, and rubato.

Music improvisation

Improvisation of music is a complex creative process that AI has successfully managed to model. It is more complex than composition. Before the 20^th century, humans did all improvisation, composition and performance. However, after that, performers have faced a rise in recording techniques that have constantly delivered flawless performances. With the rise of technology, composers have abandoned serious improvisation of music or performance. On the other hand, procedures and mathematical models that mirror technological developments fascinated modernists. With the ultra-modern compositions demanding high modernism and machine-like accuracy from performers, there was a demand for robotic precision.

Visual arts and AI

Professionals in visual arts use AI to create artworks. With AI, users can construct personal datasets to help find underlying concepts. Through AI, hidden traditional clues can be identified to help expand the limits of artwork. AI can also be used to breed two images to create new ones. Stylize images using applications like Google Deep Dream and apply different styles to works of art like pictures using AI image generators.

Furthermore, AI can be used to create a portrait one's self or a picture. Modern AI tools for art need minimum direction, and they can generate new concepts independently. With the abilities of this software, users can produce art that is meaningful because it is drawing its knowledge from human experience.

Art was traditionally known as the domain of artists — people who had trained, honed their craft, and often spent years perfecting their skills. Today, however, we are seeing significant changes in who is a “true artist”. Art has become democratized. Now anyone can create art using relatively inexpensive consumer technology. In fact, it is difficult to imagine an art studio that does not have some sort of consumer technology available for use.

There are many ways to create art that bring a new level of accessibility to the work itself: the Internet has made it possible for anyone with a computer and an internet connection to create art; low-cost consumer technology and mobile devices allow anyone anywhere to access art at any time; digital file sharing allows anyone with access to a cell phone or smartphone to create art on-the-go; Instagram allows anonymous sharing across platforms; Flickr allows anonymous sharing across platforms as well as allowing one’s photos to be shared and viewed by everyone; video sharing sites such as YouTube allow anonymous and semi-anonymous uploads which allow one’s work to be discussed and viewed by others at any time; online stock photography sites such as Shutterstock allow one’s work to be displayed without attribution or payment; and so on. Art is becoming democratized, but also more affordable for everyone. However, the democratization of art does not mean that everyone with talent should get paid for their work. Rather than assuming traditional methods will prevail in the future, it is important that we recognize this change today while taking steps forward.

What is AI?

Artificial intelligence (AI) is a hot topic these days. AI has the potential to change everything from the way we consume art to how we make and sell it. Artificial intelligence (AI) is the ability of machines to perform tasks that are normally considered human. The term artificial intelligence refers to the branch of computer science which studies, develops and uses intelligent systems based on intelligence as a fundamental concept. Artificial Intelligence is not limited to machines capable of speech, mobility, or autonomy but also includes self-learning systems and self-repairing systems (such as self-healing robots). Artificial Intelligence is not limited to robotics, but can be applied in other fields as well such as medicine and business. Artistic AI is an example of AI that uses machine learning algorithms to create artworks from a database of thousands or millions of images already created by humans. Not only does artificial intelligence allow machines to learn from previous artworks, it can also improve upon them by creating new artists in its own image... But what if you could go beyond simply looking at thousands or millions of images? What if you could actually create something? You can! Artificial Intelligence allows you to create your own artwork by creating art using data sets prepared by artists who have gone through years of training. This can be done without any training whatsoever - all you need is an idea for something and an input data set containing images previously created by other artists. It's the same technology used in the creation of modern graphics cards and video cards used in computers today!

How is AI changing the art industry?

Artists have long used artificial intelligence (AI) to create works of art, but not without controversy. A new generation of artists is using computer algorithms to create works that are both beautiful and infused with new ideas. Artificial intelligence (AI), and the ease of creating art through AI, may be a double-edged sword for artists. On the one hand, it allows them to create work that is both beautiful and new, but on the other, it also enables them to make work that sells for millions and can contribute to revenue growth. While AI has been used by artists in the past, this is an entirely new platform for artists — or at least a different kind of artist — who may not have been able to reach their audience with traditional art techniques. The rise of AI through machine learning has also drastically changed how art is viewed by consumers. In the past, consumers were only exposed to art objectively through a museum or gallery setting where they could touch and feel the artwork in person. Today’s consumers can easily access art online where they can see any artwork they want at their own convenience — whether they want it or not! Artificial intelligence has allowed some new technologies and advancements in technology to allow more freedom for artists such as drones and 3D printing. It’s now possible for an artist who has never painted before to design his own painting using software programs that enable him to create abstractions from nature as well as classical paintings from human life experiences such as films or books. This democratized access to these kinds of works is exciting for an artist who previously had little choice than just painting what was handed down from generation-to-generation without much creativity involved — like the old days when one had to sell his grandmother’s attic paintings at auction!

How can artists use this technology?

Art is a unique medium. It is a form of human expression, and an intrinsic element of self-expression. It is also an inherently emotional experience that can be experienced in different ways. Artists will likely use AI to create art, just as they do today with Photoshop or Illustrator. However, this technology won’t be used like it is today: artists will no longer be the ones who construct art and make decisions on how to create it. Instead, AI will create art on demand. The artist (or musician or dancer) will simply set the parameters for how their work should be created, and AI will do all the work on showing their work online or creating materials for sale. Artists can even set up a schedule so that they can generate art anywhere and at any time. Artists have been generating art through software applications for years now, but what if you could actually generate your own artwork by setting up a machine learning algorithm? What if you could set up an algorithm that would generate or reproduce your artwork? Here’s how:

What does this mean for the future of art?

Artists who have always been at the front of this movement are now seeing that, with the advent of AI and machine learning, they may not be needed at all. They’re already being replaced by algorithms. Artificial intelligence has been creeping into many fields. It is starting to take on an important role in the art world. AI is helping humans create art and engage in collaboration, but it’s also helping human artists make art themselves. As artificial intelligence becomes a more regular part of our lives, this is changing not only the world of artists but also our perception of them. This new era may be a good time for artists to think about how they use AI in their work and see where it can best benefit them as an artist in terms of workflow, service and communication. Art + technology = better art for everyone

Conclusion

Artificial intelligence (or AI) is a field of computer science that deals with the creation of intelligent artificial agents (AIAs) on computer systems. The term is typically used to refer to programs that are able to interact with humans, although it is sometimes also used to refer to a broader class of environments in which such interactions are possible. The term artificial life has been defined as coined by Nikos Salingaros as an "artificial life" (and later expanded upon by others), not in the sense of life or living organisms, but rather as a type of artificial system. Artificial intelligence takes a wide range of different forms, depending on the goals and requirements. AI agents can be developed for their own sake, or they can be used as part of other systems, known as servo-artificial intelligence (SAI). Examples of SAI include autonomous vehicles, robotic anthropomorphs, and expert systems for biomedicine. One area where AI is having an impact on the art industry is through its use in areas that involve image recognition and digital painting. This area includes things like digital painting software which can create images from scratch at the click of a button or an easy-to-use painting app that can be downloaded for free from either iTunes or the App Store.

 Modern Hire, a leading provider of an enterprise hiring platform for video interviewing and pre-hire assessments, today announced that its Modern Hire intelligent hiring platform for SAP^® SuccessFactors^® solutions is now available onSAP^® Store, the online marketplace for SAP and partner offerings. Through trusted science and proven technology, the platform integrates with SAP^® SuccessFactors^® Recruiting to enable hiring teams to quickly identify best-fit candidates and automate screening, interviewing, and assessment workflows through a streamlined and efficient process across systems.

“Hiring teams are consistently faced with the challenge of building a pipeline of great talent during one of the biggest labor market shortages, while also striving to meet business expectations around growth, diversity, and culture,” said Dusty Woodall, vice president of technology at Modern Hire. “As more organizations adopt automated AI tools to help meet these expectations, it’s essential to have a seamless cross-platform process in place to move candidates quickly and confidently through the entire recruiting and hiring funnel. The integration of our offering with SAP SuccessFactors Recruiting and availability on SAP Store further demonstrates our commitment to enabling a friction-less process for efficient, effective, and fair enterprise hiring.”

Through the integration of the Modern Hire platform with SAP SuccessFactors Recruiting, customers can expect to receive several benefits in their hiring process. The platform delivers the following capabilities to hiring teams:

• Better hiring decisions through data-driven experiences and responsible integration of trusted science and proven technology, simultaneously positioning the enterprise brand as innovative, forward-thinking, and an attractive company to candidates
• Effective, continuous interview process with automated scheduling, calendaring system integration, automated candidate advancement based on criteria like reviewer feedback, assessment results, or other feedback, and on-demand interview scoring
• Significant time savings by enabling on-demand interviews as well as science and data-based candidate prioritization, Modern Hire’s pre-employment assessments help teams efficiently identify top candidates, illuminating their strengths and opportunities

SAP Store, found at store.sap.com, delivers a simplified and connected digital customer experience for finding, trying, buying, and renewing more than 2,000 solutions from SAP and its partners. There, customers can find the SAP solutions and SAP-validated solutions they need to grow their business. And for each purchase made through SAP Store, SAP will plant a tree.

Modern Hire is a partner in the SAP PartnerEdge^® program. Integrated with SAP SuccessFactors solutions, the Modern Hire platform fuels customers to become intelligent enterprises. The SAP PartnerEdge program provides the enablement tools, benefits, and support to facilitate building high-quality, disruptive applications focused on specific business needs – quickly and cost-effectively.

More information on Modern Hire’s integration can be found on SAP^® Store which provides easy access to solutions built by SAP and its partners. For more information on Modern Hire’s award-winning, science-based enterprise hiring platform, please visit: https://modernhire.com/platform/.

What needs to be done to make AI fair, reports Nature.

Beginning in 2013, the Dutch government used an algorithm to wreak havoc in the lives of 25,000 parents. The software was meant to predict which people were most likely to commit childcare-benefit fraud, but the government did not wait for proof before penalizing families and demanding that they pay back years of allowances. 

Read the article Nature

Artificial Intelligence can help fight wildfires in the west, reports Aspen Times. 

With wildfires becoming bigger and more destructive as the West dries out and heats up, agencies and officials tasked with preventing and battling the blazes could soon have a new tool to add to their arsenal of prescribed burns, pick axes, chain saws and aircraft.

Read the article Aspen Times

According to Analytics Insight, artificial intelligence could help create a parallel universe. 

The idea of parallel universes has captivated the imagination of scientists, novelists, and screenwriters for decades. Scientists today are working on calculations for the creation of new universes. 

Read the article Analytics Insight

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

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.

As data science keeps becoming a go-to profession for many, you might be among those who want to become part of the new professions like Machine Learning Engineer. If so, you are joining a list of many people who want to know more about technologies like Artificial Intelligence, Machine Learning, the Internet of Things and how Data Science can help an organization. Although many people talk about these professions and buzzwords, most of them do not know what it takes to succeed in the field of data science. As an aspiring enthusiast who wants to get the best out of the new professions, know these things to succeed as a machine learning expert.

What is Machine learning?

Machine learning is a technology that is focused on helping machines learn independently from data without human input. The machine learning field is connected with artificial intelligence and is a crucial area in data science.

Who is a Machine Learning engineer?

With the crucial link between Data Science and Machine Learning, Machine Learning Engineers, Data Scientists, and Data Analysts often have overlapping job descriptions. However, the differences are in what each one of them focuses on. For instance, Data Scientists and analysts mainly focus on gathering insights from data and presenting them to the organization's leadership, who use the data to make critical decisions. Data Scientists and Analysts have some knowledge about machine learning algorithms. On the other hand, Machine Learning engineers are focused on Machine learning. Their goal is to create software components that can work with limited human supervision to gather insights from the data provided. Here are some skills you need to have as an ML engineer.  

1. Applied Mathematics

Mathematics is an important skill for an ML engineer. It helps ML users to select the correct ML algorithms for data. Maths can also help in setting parameters, approximating levels and performing statistics. Some topics that one has to be conversant with include statistics, calculus, linear algebra, multivariate distributions and probability. On top of maths, physics concepts can also be beneficial if you want to become a successful data engineer.

2. Computer science fundamentals and programming

This is another key requirement for a machine learning engineer. You must be familiar with computer science concepts like data structures, algorithms, and space and time complexity. If you have studied a bachelor's in computer science, this should not be new. You should also be well versed in different programming languages like Python, R and ML Statistics. Others like Spark and Hadoop, and SQL for database management, are also crucial for an ML Engineer.

3. Machine Learning Algorithms

 As an aspiring ML engineer, the most important skill you can possess is ML algorithms. You should understand all common ML algorithms so that you know when and where to apply. The classes of algorithms include Supervised, Unsupervised and Reinforcement Machine Learning Algorithms. Some common algorithms include Naïve Bayes Classified, K Means Clustering, Apriori Algorithm, and Random Forests. You should have sound knowledge of these algorithms and others before starting the journey.

4. Communication skills

While hard skills are crucial for an ML engineer, soft skills are equally important. If you want to change the direction of your career for the better, you should have good communication skills. These skills will make a world of difference in your Machine Learning Engineering job. This is because, in addition to understanding the data and insights obtained using machine learning, you can convey the insights to both technical and non-technical teams with ease. Furthermore, you will be able to communicate with the shareholders and clients. This is known as data storytelling, where you can present data in a storytelling fashion, which is easy to understand.