The survey conducted by the Bitkom industry association in the spring of 2021 underlines the attention AI systems are attracting companies. Two-thirds of the companies surveyed consider AI the most important future technology, and one in four companies wants to invest in this area. One-third expects expert knowledge through artificial intelligence, which otherwise could not be obtained in this form. The expectations of predictive analytics are high. The hope lies in using fewer resources, developing new products and solutions for customers, or making processes more efficient.

It is not uncommon for disillusionment to set in after introducing a system if the developments predicted by the AI ​​have not materialised or there does not appear to be any particular gain in knowledge.

AI Cannot Perform Miracles

Anyone who deals more intensively with artificial intelligence knows that the systems are only as good as their foundations. The underlying algorithms are developed by people who risk being subject to cognitive distortions. The ‘bias’ phenomenon has already been discussed in the media, for example, in connection with systems that deal with automated credit checks. Since people make AI, it cannot act impartially. That’s because developers’ biases flow into programming, whether intentional or unintentional. The unconscious or conscious data ethics of the developers also influence the results.

If the desired results are not achieved in an AI process, this can also be due to the quality of the input. AI is no different from image processing in this respect. She can’t turn a bad photo into a masterpiece. Neither can AI deliver a great result from a lack of input.

Also Read: With Artificial Intelligence Against The Shortage Of Skilled Workers

Determine And Improve Data Quality

When using artificial intelligence, the path to convincing results begins with merging the data and the ETL process (Extract, Transform, Load). A good data strategy in terms of data quality is crucial here.

Criteria for measuring data quality are:

• Completeness: The concept of completeness refers to different dimensions. Data is considered complete when content and data have been ultimately adopted in an ETL process. The business rules of the company define when 100% completeness is achieved.
• Correctness: Put, a data set is correct if it corresponds to reality.
• Consistency: The properties of a dataset must not have any logical contradictions with each other or with other datasets within a data source.
• Uniqueness occurs when the (natural) objects described by the dataset are only represented once.
• Conformance: The data must conform to the defined format.
• Validity: The data correspond to the defined value ranges.

The quality of the data can be determined and improved by rules. These rules fall into two groups:

• Formal-technical rules and
• content rules.

Formal-technical rules can be implemented easily in software whose data will form the starting point for analyses. A simple formula is sufficient to check whether a data record is complete (e.g., the quotient of the filled attributes and the number of existing features). There is also technical support for content-related rules. Plausibility checks when entering data prevent incorrect entries and thus automatically contribute to improving data quality.

The core issue is that AI systems can only deliver correct (and unbiased) results if data is available in a clean and suitable format. This is where the first fundamental mistake when using AI analysis usually lies. Based on use cases, the data quality must be measured. The results then flow into a GAP analysis that shows whether additional data needs to be collected or where there are still gaps.

AI Projects Are Not Purely IT Projects

A second significant error, which is closely linked to the lack of data quality, is a misunderstanding of AI projects. It’s just not the case that this is a pure technology project. The attitude that IT and contracted service providers often set up a turnkey system consisting of technologies and software and the rest runs by itself is inevitably misleading. The need to break down existing data silos is usually understood from a technical perspective. However, it takes more to succeed in AI analytics projects than just letting the data flow freely. The boundaries inherent in the metaphor of the silos must also be torn down in people’s minds. AI projects are also changing projects.

Data Owners Do Not Know What Quality Data Should Have

The aspect of data quality also accompanies this. The required data often belongs to a department (data or information owner), which is also responsible for data quality. This often leads to a misunderstanding of the need for good data quality. There are no bad intentions behind this: The people involved in the department do not see the advantages that result from this for their department. It is often seen as an ‘on top’ job that doesn’t add value. As a result, there is no motivation to increase data quality.

Therefore, it is not enough to tear down the data silos in the form of data lakes and then leave the data scientists alone with them. The departments as data owners must also be convinced that better data quality brings them advantages, i.e., real added value can be achieved.

The Data Product Team Connects Technology And Corporate Culture

Therefore, successful data analytics projects are also about corporate culture and the definition of common goals. This works best when work is data-centric, i.e., the stakeholders from the departments, data scientists, and data engineers act together as a so-called data product team and work together on defining the use cases and developing specific questions. The direct project work conveys the importance of data quality more sustainably to be able to initiate measures for improvement. The focus here must always be on the wishes and requirements of the department because, ultimately, they will later have to continue to support the ongoing processes and work with the information. A high data quality and thus meaningful AI analysis can only be achieved in a team.

Also Read: How Artificial Intelligence Could Revolutionize Font Design

The post Good Data Quality – The Basis For Successful AI And A Good Corporate Culture appeared first on Tech Buzz Feeds.

The shortage of skilled workers in the healthcare sector has been hotly debated in the past two years. Higher wages and bonuses were discussed to make the job attractive again. However, appropriate solutions only help in the short term, as the additional money cannot counteract the demographic change. It can be assumed that the situation will deteriorate and that the already small number of specialists will have to cope with an increased number of patients with complex diseases.

Prof. Ulrike Attenberg, Director of the Clinic for Diagnostic and Interventional Radiology at the UKB, and Prof. Together with the Cyber ​​Security Cluster have launched an AI lighthouse project. “Using the latest technologies such as B. 5G, artificial intelligence, and surgical robotics, the UKB as an innovative medical campus should be a pioneer for a medical campus of the future,”. “It’s about a secure, comprehensive digitization solution that optimises the diagnostics and treatment of patients, including robotic surgery, as well as general processes in clinics.”

With AI-Based Algorithms For Successful Therapy

All medical patient data must be available in digital form for the project. They form the basis for developing AI-based algorithms that contribute to therapeutic decision-making. For example, therapy simulators are to be used. The aim is to test the success of the therapy on so-called digital twins, i.e., digital replicas of the patients. The surgical robotics are also to be optimised.

But the UK’s efforts do not end there: the project also deals with AI-supported intelligent resource planning and smart parking. In addition, autonomously driving shuttle vehicles are to be tested on the premises of the university hospital. Finally, the digitization solutions are also to be integrated into the smart city approach of Bonn.

However, a stable and fast internet connection is required to carry out such projects. For this reason, a 5G campus network from Deutsche Telekom was established on the entire Venus campus last year.

Safety First

Adequate security precautions are essential, especially when it comes to sensitive patient data. After all, the last few years have shown that hackers are increasingly targeting medical facilities. Therefore, the University Hospital works closely with the Cyber ​​Security Cluster Bonn on the security strategy. “We are proud that, with our cluster members’ knowledge and extensive experience, we could contribute cyber security by design right from the start when designing the Innovative Secure Medical Campus.”

Millions In Funding From The Ministry Of Economic Affairs

“By using state-of-the-art technologies and digitization strategies, we will turn the vision of a clinic of the future into reality within the next three years.” “As a pilot clinic, Bonn will be a pioneer for state-of-the-art, highly efficient, and digitised patient care and set new standards. This new standard will not only make work easier for doctors, nurses, and everyone involved in healthcare but also for better processes,

So that other hospitals can also benefit from the advantages of the Innovative Secure Medical Campus. The project will be included in the AI ​​flagship initiative of the competence platform for artificial intelligence NRW (KI.NRW). “The approach of combining state-of-the-art AI technology with the topic of cyber security and data protection from the outset shows: NRW can do AI. But also: AI is a team sport.” “The interplay of different expertise enables innovative solutions with appeal beyond NRW.”

The post With Artificial Intelligence Against The Shortage Of Skilled Workers appeared first on Tech Buzz Feeds.

It could be a revolution that researchers at the Ecole Polytechnique Fédérale de Lausanne (EPFL) have achieved. They worked together with the ECAL master’s program in Type Design to do this. It’s about art, but most of all, about the process of creating digital fonts. They have developed a machine-learning algorithm to generate Chinese characters (Hanzi) as images.

For example, to create an alphabet in the font design, each letter shape must be drawn in a uniform style. The so-called glyph is the basic symbol in every letter form. It is a legible sign for writing. For example, if you use the Latin script, at least 26 capital letters are required for the design. The Chinese Hanzi script has 6,763 commonly used characters and tens of thousands of characters for more complicated sentences. If you were to create a typeface conventionally, it could take years.

AI: Useful Support In Font Design

Shuhui Shi has been designing fonts for two years. “When I draw Latin scripts, a few dozen glyphs are usually enough for everyday use. However, when I tried to apply my knowledge of font design to the Chinese language, I found that it was almost impossible to complete a standard character set with 6,763 glyphs on my own,” explains the student. And she was not alone in this problem, as she found. Many other designers fared similarly.

With such a large number of glyphs, artificial intelligence might help. This is how the so-called AIZI project came about, in which the EPFL Computer Vision Laboratory played a key role. First, the researchers developed glyphs that they could use to train the AI. To do this, the student created a large database of characters that could be broken down into very simple components. Their idea: A new font only requires a few basic components to be designed. They could then be put together automatically to form all the fonts. “We then developed algorithms to do the second part, namely to create different characters based on these components.”

AI Could Make The Job Of Designers Easier

This algorithm can produce tens of thousands of hanzi as images. The result: Chinese characters can be created in this way. It takes less than 500 Hanzi entries for this. According to the researchers, the algorithm path represents a contemporary, transdisciplinary approach that stands at the interface between design and technology. “Although we’ve used the technology called General Adversarial Network or GAN many times, working on such a challenge was a first. GAN is a deep neural network that aims to generate images such as faces. Then, our idea was to use this type of technology to generate characters”.

Shuhui Shi has come to completely new perspectives through collaboration. An AI assistant can help designers create an entire font with fewer than 500 human-designed hanzi. “We currently have a database with more than 90,000 hanzi and tens of thousands of well-scaled, generated hanzi in the left-right composition”. However, the results would have to be revised and optimized again. As soon as the student is back in China, she wants to continue studying machine learning and font design. She hopes that this first step will make it easier for other designers to get started. The use of technology makes it possible to concentrate more on creative ideas without designing thousands of characters. Perhaps this way, more people would try their hand at their first Chinese font.

AI Could Also Preserve Cultural Heritage

For Matthieu Cortat-Roller, head of the master’s program in Type Design, this new approach has an even deeper meaning. He cites the example of the Syrian script, which is only used by very few people today, as a large part of the Syrian population stayed in other countries due to the war in their home country. “They worry about the future of their culture and identity. It’s not that easy to get a writing system when you live in the diaspora”. AI can help preserve cultural heritage.

The post How Artificial Intelligence Could Revolutionize Font Design appeared first on Tech Buzz Feeds.

According to experts, the world market for process automation will be between $ 40 billion and $ 42 billion this year. And it will continue to grow at an estimated rate of 8% per year. The most substantial growth is expected in Asia. The world market is shared by manufacturers from America, Europe with the Middle East, Africa and Asia.

US companies are currently at the forefront of IT solutions, while Europe is ahead in process-related components such as sensors and Fieldbus devices.

This year’s Interkama President, Dr Uwe Martens, identifies several factors as the causes of the growth expected for the beginning of the decade:

• Cost pressure in the chemical industry will lead to increased automation in the manufacturing processes
• Increasing demands on safety and environmental standards also in the young industrialized countries
• Increased functionality thanks to IT technology and digital communication.

The interaction between the automation markets and the process markets is driving this change, for example, in the chemical industry, pharmaceuticals, food, oil and gas, paper and pulp, and water and waste management.

Also Read: Automation: Why The New Era Calls For A Change In Corporate IT

More Information About The Technology Drives The Automation

At the same time, the markets are facing a dramatic change in their structures.

On the part of the providers, the Interkama President predicts far-reaching mergers and a drastic shrinking of the number of providers. He believes that only four will remain of the ten global generalists in today’s automation market. The number of process equipment suppliers will even shrink from 4,000 to just 400.

New fields of work arise for the automation technology suppliers through diversification into previously foreign industries. There are opportunities for automation in building services, such as remote meter reading for gas, water and power supply, facility management, plant operator companies, etc.

A change in values ​​can be observed in automation. The focus is no longer only on certain levels of a company – classically, on pure production – but rather the entire company is moving into the direction of automation efforts. According to this, automation defines itself in the future as a company network for more competitiveness, more economic efficiency, economical use of resources, quality assurance, more security and adequate environmental protection.

“Innovation in automation is happening today,” “not least driven by the progress of information technology, that is, by the progress of microelectronics and system technology, communication technology and software.” According to Steusloff, information technology is also the essential basis for system solutions to emerge, which take into account and comprehensively meet users’ needs.

The presentation of such system solutions will be an outstanding feature of this year’s Interkama. The user/visitor should not only be presented with an extensive range of components that he can integrate himself, but the complex integration task itself will also be the subject of Interkama.

As an example of the information link within the entire company, Steusloff cites the use of data from measurement and automation technology to validate production processes. And the quality of the automation processes can also ensure the product quality, which is indispensable, for example, where legally regulated markets are involved – for instance, in the pharmaceutical industry.

Here, the verification of the product properties is indirectly based on the information generated in the individual measurement and control devices of the automation systems. For this, however, it must be ensured. The control quality is understood in its complex effect on the product quality.

Conclusion

Scientists are in demand here, and they can get new ideas from the exhibitors. Because in teaching measurement and automation technology, with the rapid progress of the supporting technologies from information technology, constant updating of the teaching content is necessary. Measuring, controlling and regulating are primarily seen in connection with the information implemented in the process. There will be an opportunity to discuss this new understanding of automation at the fair.

Also Read: Advanced Space Technology And Process Automation

The post Information Technology Drives Automation appeared first on Tech Buzz Feeds.

Automation: Corporate IT Is More Powerful Than Ever

The IT of many companies has never been as efficient as it is today. Technologies such as virtualization, cloud computing, IoT, big data, and machine learning already make companies more powerful and efficient and give an idea of the influence of future technologies such as artificial intelligence. A side effect of all these technologies is that their use has increased the complexity of the networks required for them in recent years. At the same time, organizations are constantly confronted with making important decisions in a very competitive environment. One approach to being able to process data quickly and precisely in very complex networks is automation.

What Automation Means In IT

Automation in IT is not a technology or a solution that you buy and install. Instead, it is a culture built on the fact that repetitive data processes in the data center are automated. Automation helps to evaluate and process data in the shortest possible time. This means that the processes for data processing run automatically in the background without manual intervention. This is no longer an option in many organizations because in times of big data, countless data sources, and an acute shortage of skilled workers, it is practically impossible to process all data manually.

For example, in the stock markets, commodities and companies are valued within milliseconds, and it would not be possible for humans to process data that quickly. But even outside of these extreme industries, organizations of all kinds need the correct information as early as possible to make precise decisions.

Focus On Optimizing Data Processes

To better master the challenge of processing data faster and making better decisions, many companies have already started to optimize their data processes. As a first step, organizational changes have already been made in many organizations, and a new, data-centric position was created, such as that of Chief Data Officer (CDO). But the new CDOs of many companies encounter resistance from unexpected quarters in their efforts to streamline and automate data processes because many employees in IT fear for their jobs.

The Consequences Of Automation For Jobs

One would assume that IT employees would receive the help of new solutions and approaches such as automation, machine learning, and automation with open arms. However, the belief persists in many places that the new, clever helpers endanger workers and replace people sooner or later.

History teaches us that this simplification is not valid. For example, the industrial revolution of the late 19th century replaced many traditional jobs with machines, but unemployment before the corona crisis was almost identical to that in the 1920s. And that on the assumption that organizations now have more machines and automation than they did then.

Automation and machine learning are used more and more frequently in IT and are viewed critically by IT employees exposed to the risk of being replaced by clever algorithms sooner or later. So that this perception does not become a blockade for more automation, executives must convince the IT workforce of automation and machine learning advantages.

What Speaks For Automation In IT

Analysts from various think tanks have analyzed the benefits of automation in IT over the past few years. They predict that automation will increase global GDP by up to $ 15.7 trillion by 2030. Concerning the work of the on-site IT teams, the analysts concluded that automation does not replace their IT teams but merely expands the tasks of existing teams.

In particular, automation helps IT teams replace their manual, repetitive, and, frankly, tedious tasks with strategically more valuable projects to the company. In the end, the staffing level remains the same, with its members having to do fewer simple tasks instead of concentrating on more exciting aspects of their work.

Strategic Management Of IT In The Age Of Automation

The first step in making your business more automated is understanding that automation is not a solution that you buy and use. Instead, automation is a culture of the IT organization to automate all processes – as far as possible. To establish this culture, the management has to lead the way and convince the IT department of the importance and actively support them. Significant support for IT would be to develop the skills to implement automation projects. Many IT departments are not doing very well with these skills.

Many IT professionals believe that they do not have the skills necessary to implement the latest technologies in the business. Even worse: In many companies, IT people are so stressed that their work takes away the allotted time for training. However, without training in new technologies, the problem only gets worse. So the right strategy is to invest in appropriate IT training. Training employees to implement and work with automation-based systems will be beneficial to employees personally, to the IT team, and ultimately to the organization.

Bring Everyone Involved To One Table For Automation Projects

Automation is not a task that only affects part of IT. Instead, different employees have to be won over to the topic. At the beginning of every automation project, everyone should be brought together around the table. CTO, CDO, and CIOs should sit down and discuss how automation can be incorporated into existing business modernization plans. What do you have to pay attention to, and what resources are required? Which individual stages of the project are to be taken to keep possible interruptions in operation as low as possible.

Executives who understand the value of automation to their business need the right strategy to anchor it properly. Without the right strategy that convinces, empowers, and actively supports the IT workforce, the project runs the risk of being implemented only half-heartedly or failure. In addition to establishing a culture of automation, managers must, above all, invest in the training of their IT teams and be involved as an active part of the project.

The post Automation: Why The New Era Calls For A Change In Corporate IT appeared first on Tech Buzz Feeds.

The topic of AI has reached people’s minds, and is one of the most important results of the study. For example, 83 per cent of those surveyed said that technology is a decisive competitive factor from their point of view. It plays an indispensable role in product innovations for 64 per cent. Accordingly, almost half will have the topic of artificial intelligence high on the agenda for the next three years.

However, there is often a lack of practical experience: Although every fifth company surveyed has already implemented chatbot projects, many other AI applications are only in the planning phase. The respondents recognize that AI technologies allow new processes and offer in marketing, sales and service. For example, 85 per cent agree that AI processes in digital marketing reduce wastage and thus save costs.

At 54 per cent, most respondents can imagine using AI-based recommendations to develop a new product or service – 32 per cent have already implemented it or are in the middle of planning. Intelligent lead scoring, which predicts the conversion rate for each customer, is considered a good idea by 56 per cent – but only 26 per cent of those surveyed are in the process of implementing or planning.

Consumers See AI Positively

What companies can imagine and what consumers would use are also not always the same. For its AI study, Adesso asked those responsible for evaluating specific use cases from the perspective of their industry: Do you think about a similar AI solution? Is such a solution interesting for your customers? Eighty-four per cent of decision-makers in the healthcare sector are convinced that consumers would use AI-supported health programs. However, at 38 per cent, they are far more cautious.

Seventy-six per cent of decision-makers in the financial sector assume that bank customers will entrust their investments to an AI application. But only 30 per cent would use such a system. Eighty percent of insurance experts can imagine a purely virtual insurance offer without people and instead with AI for their customers. They are more reserved at 46 per cent. In general, the mood among consumers is good: 83 per cent are convinced that AI and robots will take on many annoying tasks in the future and make life easier. Sixty-one per cent believe that AI will bring them benefits.

It Doesn’t Work Without AI Expertise

The decisive factor in successfully implementing AI projects are qualified employees. However, in companies, there is often a lack of AI skills, as the executives surveyed are aware of. More than half of the respondents want to make their employees fit for the topic of AI. Forty-four per cent are in the process of hiring appropriately qualified experts.

“The company decision-makers have recognized that AI technologies determine the success of a company in an increasingly tough competitive environment. Most of the companies surveyed have the topic on their schedule, the starting position is good: Consumers have a positive view of artificial intelligence. However, companies have to ask themselves when and where AI technology makes sense. There is no such thing as one AI. Every company has different goals, different framework conditions and needs a system that is tailored to its application,”

“Building such a system is an exhilarating task. The step from planning to the actual AI project is not without it. There is a lack of experience and blueprints. AI solutions place completely new demands on the project structure and the specialist knowledge of those involved”.

The post Artificial Intelligence Lacks Practical Implementation appeared first on Tech Buzz Feeds.

The baffling word “prophet” is derived from the Latin “prophet,” which signifies “divine beings saying.” It is implied that you ask higher forces past everyday reality and get replies to consuming life inquiries from them. Now and again, likewise with the famous prophet of Delphi, where the prediction is made is additionally implied.

Oracle: This Is How It Works

One requirement is a specific medium or an extraordinary custom for a prophet to get a demonstrative sign. The medium can be a straightforward “yes-no-perhaps” 3D shape from the Christmas market, answering basic yes-no inquiries. Or on the other hand, a pendulum whose roundabout or straight motions point the examiner a specific way.

The prophet of fortune-telling cards, which incorporates tarot, Lenormand, or kipper cards, straightforward skat cards, holy messenger cards, and runic prophets or I-Ching visionaries in card structure, is exceptionally well known. These cards are either drawn independently for a particular inquiry or as an everyday card for a short forecast. They are fanned out as indicated by a specific format arrangement wherein the card’s position gives it proper importance.

Free guide prophets and other free prophets can be discovered all at once on the web. However, on the web and disconnected, every inexplicable sign is just pretty much as great as the translation you give it. You can’t check out destiny. In any case, prophet cards like the tarot and its family members frequently give you valuable ideas in their emblematic symbolism. What is generally significant in your life for sure you need to choose earnestly if there should be an occurrence of uncertainty.

Prophet improved the MySQL HeatWave In-Memory Query Accelerator in the information base assistance by utilizing advanced AI in Oracle Cloud. The organization additionally said the upgrades don’t imply that MySQL Database Service attacks its leader Oracle Database.

Exhaustively, the organization dispatched MySQL Autopilot on August 10, a part of HeatWave that utilizes advanced AI methods to speed up question execution and adaptability. MySQL HeatWave works with MySQL Database Service on Oracle Cloud Infrastructure (OCI) to speed up OLTP and OLAP blended examination and responsibilities.

Included with HeatWave at no extra expense, Autopilot mechanizes parts of accomplishing superior inquiries at scale, including provisioning, information stacking, questioning, and blunder taking care of. Progressed strategies are utilized to test information, gather and question insights, and construct AI models using Oracle AutoML to show memory utilization, network load, and runtime. Autopilot expands the understanding of the HeatWave Query Optimizer as more inquiries are run, bringing about persistent execution improvement, as Oracle said.

Oracle MySQL Autopilot, The Features

Critical components of MySQL Autopilot incorporate programmed provisioning by foreseeing the number of HeatWave hubs needed to run a responsibility utilizing versatile information testing that requires examination. Programmed equal stacking to advance burden time and memory use by foreseeing ideal parallelism for each table stacked into HeatWave. Programmed information situating by foreseeing which segment tables ought to be divided in memory for best inquiry execution.

The programmed gauge of question time before inquiry execution. They programmed mistake recuperation to arrange new hubs and reload the essential information if one corner became inert. Regardless of the expanded abilities of its MySQL data set line, Oracle demands that there is no attack of its Oracle venture data established administrations. Prophet Database is for enormous scope endeavor arrangements, while MySQL is intended for engineers, cloud-local open source applications, and organizations that have never had an on-premise climate.

For instance, an Oracle Exadata cloud administration may have a 2.5-petabyte information base, while HeatWave is restricted to 32 Tbytes. An enormous public monetary administrations firm would utilize Oracle Autonomous Database or the Exadata cloud administration, however not normally MySQL with HeatWave, because of the sheer size of the dataset, as per Oracle.

Notwithstanding the presentation of MySQL Autopilot, Oracle has presented MySQL Scale-Out Data Management to develop information further reload execution in HeatWave up to multiple times. HeatWave would now be able to help a group size of up to 64 hubs. It had recently been restricted to 24 bunches. A heatWave can likewise measure up to 32 Bytes of information, contrasted with the past 12 Tbytes.

Also Read: 5 Machine Learning Benefits That Are Poised To Boost SME’s Growth

The post Oracle Accelerates MySQL HeatWave Queries With Machine Learning appeared first on Tech Buzz Feeds.

As space technology develops, scientists are able to collect more and more data from space. Process automation can help researchers wade through this information and can use AI to develop faster, more large-scale ways to collect data about space.

Space technology is becoming more advanced all the time. With modern technology, scientists can mine a wealth of data about space and the universe to help us understand more about the galaxy. In fact, there is now so much information flowing from space to Earth that researchers are turning to AI process automation to help them analyze it all. Here are some of the ways that space researchers are turning artificial intelligence to their advantage.

Space Technology: Advanced Uses and AI Development

Innovations in space tech mean that we can rapidly collect huge amounts of data about phenomena that occur in space. Through satellite use, scientists can observe planetary movement, discover black holes, and observe life on Earth in new and unique ways. Meanwhile, as AI also becomes more advanced, researchers are looking at ways to combine the two fields in order to introduce process automation to their endeavours.

What Is Process Automation?

Process automation refers to the ability to automate tasks through machine programming. This can mean programming a computer to scan information or to repeat a process automatically. It can also mean programming an AI to intelligently analyze data or run complex algorithms.

Process automation can speed up tasks that would take a human team years to complete or that would be impossible for humans to complete at all. This means it can help us tackle data on a much grander scale, analyzing millions of pieces of data input in a rapid space of time. When it comes to space tech development, massive scale is required, and we rely heavily on machines because there is so much space that humans cannot explore. This is why experts are so hopeful about the use of process automation, and particularly the development of AI, in space exploration research.

Also Read: Time For Artificial Intelligence In IT Security

How Can Process Automation Be Used in Space?

Discovering Black Holes

Researchers are currently training complex AIs to recognize the gravitational waves around events like black holes or neutron stars. This can be achieved by training AIs to recognize certain ripple or vibration patterns, which occur when a black hole is present. By recognizing these patterns early, AI satellites could send a warning to astronomers on Earth. This would allow researchers to turn their telescopes and witness the black hole, thereby helping scientists to learn more about these events.

Identifying Exoplanets

While all of the planets in our Solar System rotate around the sun, exoplanets exist outside of this system and orbit around their own stars. One of the ways that researchers identify the presence of exoplanets is by charting the light from these stars. By observing changes or dips in this light, scientists can see the way that exoplanets orbit around them and learn more about these exoplanets. These types of equations are referred to as time-series analyses.

However, with advanced space tech, companies and space startups cannot keep up with the number of exoplanets they find and the amount of information about them they receive. By training AIs to use these time-series analysis techniques, researchers hope to automate the process of observing exoplanets. This can help scientists learn even more about a larger number of exoplanets in a much shorter space of time.

Locating Gravitational Lenses

A gravitational lens occurs when two galaxies line up. When two galaxies align in the perfect position, a gravitational lens is created, which allows scientists on Earth to see one galaxy through the other more clearly. These events are extremely rare and difficult to spot.

Scientists would need to comb space almost continually to find them consistently. This is, again, where AI comes in. AI algorithms can be taught to recognize these lens’ and collect images from them. This can effectively automate the process and still provide scientists with more data than they could collect on their own, allowing for a greater understanding of the universe.

Sustainable Space Exploration

As space tech develops, there is also a growing interest in creating more sustainable space technology. There have already been major strides in this field, with many companies looking into reusable satellites and modern rockets that use greener fuel sources. There is also a large degree of interest in 3D printing in space industry circles to see if scientists can generate materials more sustainably.

Space junk is a serious problem and one that the industry hopes to solve. There are currently thousands of satellites orbiting in space, which are no longer used, and which can collide with other objects. AI and automation present hopeful options in the field of space junk removal, too, and some unique experiments are already underway to test out some of these methods.

Conclusion

Space technology opens up amazing possibilities. As the technology improves, with advances like AI and process automation, these possibilities grow even further. It will be fascinating to see where the next few years take us in terms of space research.

Final Call: Are you interested in space research? How about AI? Share your space interests in the comments!

Also Read: How Artificial Intelligence Is Transforming The Recruitment Space

The post Advanced Space Technology And Process Automation appeared first on Tech Buzz Feeds.

Fleet Management

In logistics, ensuring that the products you are shipping reach their destination in optimal condition is essential for the reputation of your business. Reefer monitoring solutions are one of the best ways you can utilize AI for cost-effective control, monitoring and compliance for your refrigerated assets. When moving your assets around you must ensure you are accurately monitoring temperatures, or you run the risk of your stock spoiling. One bad shipment could destroy your fleet’s reputation, and with easy AI solutions available, it is not worth taking the risk.

Automate Repetitive Tasks

This is one of the more basic uses for AI, but it is still one of the most beneficial. Computers are able to automatically schedule meetings, transcribe and do bookkeeping. These are tasks that are tiresome for employees, and the use of AI will free them up to do other things. It also allows you to employ people in areas that need genuine skills, in turn making your business more competitive.

Analytics

AI in predictive analytics allows for computers to take over some of the most labor intensive, repetitive tasks. It is able to identify data types and find connections between different sets. One of the main advantages is AI’s ability to learn. By watching human interaction with data, the AI will learn the most common human errors and be able to correct them. This is especially useful when dealing with very large datasets. It is also able to recognize unexpected patterns in datasets and alert the user when they appear.

Cybersecurity

Having optimal security for your business is a priority if you want to protect your assets and your data. Most modern firewalls contain AI which is can monitor the network and immediately detect if there are any suspicious irregularities. This stops any malicious threats long before they can access your network. AI is also instrumental in the use of biometric logins, allowing users to login to networks through facial recognition and fingerprints, drastically reducing potential security threats.

Provide Customer Support

Most websites you visit these days will have some form of chat box which offers users support. Many of these are using AI. This tech advancement can improve customer service needs since AI automation typically is not restricted to traditional business hours. Through the use of a knowledge base, the AI is able to answer most questions and give users solutions to any problems they have. These can be especially useful for IT desks as the majority of problems they receive tickets for are easily solved, yet take up time and reduce productivity. The use of AI will remove this problem and is one of the best solutions when it comes to customer support.

Also Read: How Artificial Intelligence Is Transforming The Recruitment Space

The post How AI Can Help Your Business appeared first on Tech Buzz Feeds.

The use of technologies such as ATS (applicant tracking systems), online job boards, and networking websites have significantly enhanced the recruitment process for both employers and job seekers. However, along with an increase in the hiring efficiency, online recruitment tools also create bottlenecks such as a surge of applications, unqualified candidates for recruiters. These issues need to be addressed to make recruitment more efficient and scalable. The ideal solution is to create an AI-enabled hiring process to match the right candidates with the best job opportunities.

Here’s how recruiters can leverage AI and related technologies to attract qualified candidates and enhance their hiring processes:

Resume and Candidate Targeting

Recruiters can use AI based recruitment tools to build a database of qualified candidates by matching the company’s job description to the candidate’s resume and shortlisting the closest match found. These tools use pattern matching algorithms to shortlist qualified candidates from a large pool of applicants. Using AI, recruiters and HR managers can automate routine tasks to save a lot of manual effort and time, increase productivity, and create an efficient hiring pipeline.

Candidate Evaluation

HR professionals can use AI-powered applications for conducting pre-screening digital interviews. This process can speed up the rate at which a candidate is evaluated and eliminate any chances of interviewer biases. The recorded interviews can be shared across hiring teams to check for transparency and candidate’s adaptability in their own time.

Improved Candidate Experience

Using AI-enabled chatbots, recruiters can now manage the candidate experience by delivering instantaneous and real-time responses, sharing updates and reminders, and providing feedback throughout the hiring process. These AI-powered automation processes can significantly reduce time-to-hire and time-to-interview by providing talent acquisition experts with valuable insights and eliminating bottlenecks across the recruiting process. With the help of Artificial Intelligence algorithms, chatbots can deliver conversational experiences to drive better engagement as compared to traditional emails, build trust with candidates, and provide a seamless experience to the applicants.

Soft Skills and Personality Testing

AI-powered technologies can be utilized in the video interviewing process to analyze the candidates’ facial expressions and reactions and generate a detailed report that can help recruiters gauge features like confidence, truthfulness, communication skills, body language, etc., and make well-informed hiring decisions. Recruiters can also create emotional intelligence tests to check if a potential candidate is a good cultural fit for the organization.

Efficient Remote Hiring

Technology has made it possible for companies to hire remote developers from across the globe and scale their teams faster than ever. Talent is not limited to any geographical location, and hence companies are shifting towards remote work culture to hire talented professionals globally. To make the hiring pipeline more efficient and scalable, we need something more than the traditional job listing platforms. And that is when the concept of a deep jobs platform comes into the picture. A deep jobs platform addresses the various stages associated with the recruiting process by using AI-enabled technologies and providing in-depth analysis for efficient hiring. It provides employers with the right signals/indicators to make effective hiring decisions.

Unlike traditional job platforms, a deep jobs platform offers support across the recruiting process like vetting, onboarding, providing collaboration and communication tools, managing payments, performance monitoring, among others. Turing is one of the deep jobs platforms that help match top remote software developers to the best Silicon Valley companies. Turing implements a vetting algorithm to select the best developers from a global talent pool of 700K+ developers, eliminating the chances of cultural or gender biases. It offers collaboration tools such as The Turing Workspace and Turing Virtual Machine that provide transparency, security, time-tracking, and workforce management features to both employers and developers. It provides boundaryless job opportunities to the developers from across the world and Silicon Valley-calibre talent to the companies, making a win-win situation for everyone involved.

How Can AI Shape the Future of the Recruitment Space?

It’s evident that AI can help make the hiring process more efficient by automating routine tasks, giving more time to talent acquisition professionals to focus on value-added responsibilities. In addition to this, AI-powered applications can significantly improve the job application experience for the candidates by delivering quick responses, providing feedback, and preventing their applications from going into a “black hole”.

Using AI in the recruitment space to source the right talent will allow companies to spend more time improving employees’ experience, developing internal talent, and increasing employee retention rates. Deep jobs platforms like Turing can help companies build their team faster and grow exponentially.

The post How Artificial Intelligence Is Transforming The Recruitment Space appeared first on Tech Buzz Feeds.