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Although the COVID-19 pandemic affected many areas of industry, it did not lessen the impact of Artificial Intelligence in their daily lives. Thus, we can assume that AI-powered solutions will undoubtedly become more widely used in 2021 and beyond.

Here are the top 10Artificial Intelligence (AI) innovation trends to watch out for this year:

Knowledge will become more available in the coming years, putting digital data at higher risk of being hacked and vulnerable to hacking and phishing attempts. AI and new technologies will help the security service in combating malicious activities in all areas. With strengthened safety initiatives, AI can help prevent cybercrime in the future.

More unstructured data will be organized in the future using natural language processing and machine learning methods. Organizations can take advantage of these technologies to generate data that can be used by RPA (Robotic Process Automation) technology to automate transactional operation. RPA is one of the tech industrys fastest-growing segments. Its only drawback is that it can only work with structured data. Unstructured data can be easily translated into structured data with the aid of AI, resulting in a valuable performance.

Many industries and companies have deployed AI-powered chatbots in the previous years. Better customer service automation is possible with AI chatbots. These conversational AI chatbots will begin to learn and develop their understanding and communication with customers in 2021.

The Covid-19 pandemic is quickly shifting automation priorities away from front-end processes toward back-end processes and business resilience. Intelligent Automation can, in reality, combine robotic and digital process automation with practical AI and low-code devices. While growing their operations, these innovations will help companies become more competitive and robust.

Quantum AI is set to grow in popularity as more businesses seek to implement the technology in supercomputers. Using quantum bits, quantum computers can tackle any possible problem much faster than traditional computers. This can be useful for processing and analyzing large sets of data in real-time, as well as rapidly predicting specific patterns. In the next decade, quantum AI is predicted to make significant advances in fields such as healthcare and banking.

RPA is one of the most revolutionary AI systems for automating repetitive tasks. On the desktop, it can effectively execute a high-volume, repetitive process without making a mess. Its possible that the job entails invoicing a customer. Furthermore, it can repeat the process several times a day, freeing up human time for more productive activities.

AI is now assisting the healthcare industry in a significant way and with high precision. AI can help healthcare facilities in a variety of ways by analyzing data and predicting different outcomes. AI and machine learning tools provide insights into human health and also propose disease prevention measures. AI technologies also enable doctors to monitor their patients wellbeing from far away, thereby enhancing teleconsultation and remote care.

Artificial intelligence is a wonderful technology that, when combined with the power of the Internet of Things (IoT), can provide a powerful business solution. The convergence of these two technologies in 2021 would lead to significant changes in the automation domain.

Face recognition technology will evolve at a rapid pace in 2021 as a result of the recent Covid-19 problems. It uses biometrics to identify facial characteristics from photographs and videos, and then compares the information to an existing database.

Businesses can use edge computing to convert their daily data into actionable insights. It provides servers and storing data solutions for computers and apps to ensure a smooth operation while allowing for real-time data processing that is much more efficient than cloud computing. Edge computing will also improve the efficiency of cloud servers because it can be carried out on nodes.

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Top 10 Artificial Intelligence Innovation Trends to Watch Out For in 2021 - Analytics Insight

Artificial intelligence (AI) has transformed many aspects of our lives for the better. It even played a role in developing vaccines against COVID-19. But you may be surprised just how many things we take for granted that rely on AI.

As IBM explain, "at its simplest form, artificial intelligence is a field, which combines computer science and robust datasets to enable problem-solving." It includes the sub-fields of machine learning and deep learning. These two fields use algorithms that are designed to make predictions or classifications based on input data.

This is how AI is used in our everyday lives.

Image: European Parliament

Of course, as technology becomes more sophisticated, literally millions of decisions need to be made every day and AI speeds things up and takes the burden off humans. The World Economic Forum describes AI as a key driver of the Fourth Industrial Revolution.

Forecasted shipments of edge artificial intelligence (AI) chips worldwide in 2020 and 2024, by device.

Image: Statista

The Forums platform, Shaping the Future of Technology Governance: Artificial Intelligence and Machine Learning, is bringing together key stakeholders to design and test policy frameworks that accelerate the benefits and mitigate the risks of AI and machine learning.

Here are 10 examples of AI we encounter every day.

Your email provider almost certainly uses AI algorithms to filter mail into your spam folder. Quite helpful when you consider that 77% of global email traffic is spam. Google says less than 0.1% of spam makes it past its AI-powered filters.

But there are concerns that algorithms that read content to target advertising are invading our privacy.

AI automates a host of functions on your smartphone, from predictive text that learns the words you commonly use to voice-activated personal assistants which listen to the world around them and try to learn your keywords.

The way your phone screen adjusts to ambient light or the battery life is optimized is also down to AI. But if the personal assistant absorbs everything you say, whether youre on the phone or not, some critics say it creates opportunities for surveillance, however benign the intention.

In many parts of the world, online and app-based banking are the norm. From onboarding new customers and checking their identity to countering fraud and money laundering, AI is in charge. Want a loan? An AI-powered system will assess your creditworthiness and decide.

This is how AI is used in banking.

Image: Business Insider

AI also monitors transactions and AI chatbots can answer questions about your account. More than two-thirds of banks in a recent survey by SAS Institute say they use AI chatbots and almost 63% said they used AI for fraud detection.

Going for an x-ray? Forget the idea of a clinician in a white coat studying the results. The initial analysis is most likely to be done by an AI algorithm. In fact they turn out to be rather good at diagnosing problems.

In a trial, an AI algorithm called DLAD beat 17 out of a panel of 18 doctors in detecting potential cancers in chest x-rays.

However, critics say AI diagnosis must not become an impenetrable black box. Doctors need to know how they work in order to trust them. Issues around privacy, data protection and fairness have also been raised.

As in banking, chatbots are also being deployed in healthcare to engage with patients - for example, to book an appointment - or even as virtual assistants to physicians. This presents numerous issues though, from miscommunication to wrong diagnoses.

The World Economic Forum's Chatbots RESET programme brings together stakeholders from multiple areas to explore these opportunities and challenges to govern the use of chatbots.

AI is at the heart of the drive towards autonomous vehicles, adoption of which has accelerated due to the pandemic. Delivery services are one area being targeted, while China now has a robotaxi fleet operating in Shanghai.

There are still safety issues to be ironed out, however. There have been accidents involving self-driving cars, some of them fatal.

The Netherlands is the best prepared for autonomous cars.

Image: Statista

Conventional trackside railway signals are being replaced by AI-powered in-cab signalling systems which automatically control trains. The European Train Control System allows more trains to use the same stretch of track while maintaining safe distances between them.

To date, the use of AI in controlling aircraft has been limited to drones, although flying taxis that use AI to navigate have already been flight-tested. Experts say a human is still better at flying an airliner but AI is widely used in route planning, optimizing schedules and managing bookings.

7. Ride sharing and travel apps

Ride sharing apps use AI to resolve the conflicting needs of drivers and passengers. The latter want a ride immediately, while drivers value their freedom to start and stop working when they choose. Learning how these patterns interact, AI can send you a ride when you ask for it.

Travel apps use AI to personalize what they offer users as algorithms learn our preferences. Hotel search engine Trivago even bought an AI platform that customizes search results based on the users social media likes.

Uncanny how social media seems to know what you like, isnt it? Of course, its all down to AI. Facebooks machine learning can recognize your face in pictures posted on the platform, as well as everyday objects to target content and advertising that interests and engages you.

Job seekers using LinkedIn benefit from AI which analyzes their profile and engagement with other users to offer job recommendations. The platform says AI is woven into the fabric of everything that we do.

Unexpected breakdowns are every factory managers nightmare. So AI is playing a key role in monitoring machine performance, enabling maintenance to be planned rather than reactive. Experts say its cutting the time machines are offline by 75% and repair costs by almost a third.

AI can also predict changes in demand for products, optimizing production capacity. AI is currently used in about 9% of factories worldwide but Deloitte says 93% of companies believe AI will be a pivotal technology to drive growth and innovation in the sector.

Google says AI can enhance the value of wind power by 20%.

Image: Pixabay/enriquelopezgarre

10. Regulating power supply

Wind and solar power may be green but what happens when the wind doesnt blow and the sky is cloudy? AI-powered smart technology can balance supply and demand, controlling devices like water heaters to ensure they only draw power when demand is low and supply plentiful.

Googles DeepMind created an AI neural network trained using weather forecasts and turbine data to predict the output from a wind farm 36 hours ahead. By making output to the power grid more predictable, Google says it increased the value of its wind energy by 20%.

The views expressed in this article are those of the author alone and not the World Economic Forum.

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Did you know these 10 everyday services rely on AI? - World Economic Forum

The worlds firstin-vitro fertilization(IVF) baby was born in the United Kingdom in 1978. The success of the artificially made baby gave hopes to the human community on their fertilization and parenthood. After four decades of intense research and trials into the field, doctors now useartificial intelligenceto help parents get babies successfully. Scientists are working onembryoanalysis with computeralgorithmsto help build families.

More than 80 million couples are affected by infertility across the globe. Around one in seven couples have trouble conceiving, which means there is a high demand for solutions such asin-vitro fertilization. Creating anembryois a process where the ovum from the female ovary and sperm from the male are fused outside the body in a laboratory. Theembryois then placed in the females ovary for the development ofin-vitro fertilization. It has been reported that more than 5 million babies have been born from thein-vitro fertilizationmethod. Unfortunately, not all IVF treatments turn out to be successful. Couples who need IVF to conceive a child are well aware that even the most advanced assisted reproductive technology doesnt always guarantee a baby. Therefore, doctors are seeking the help ofartificial intelligenceto pick theembryothat is most likely to succeed. In a normal in-vitro cycle, around 70% of the embryos are abnormal, resulting in a miscarriage or a baby with a lifelong genetic disorder. Butartificial intelligencecan change the routine by detecting theembryothat is more likely to grow without any problem.

Selecting the successful embryo is the toughest process inin-vitro fertilization. Currently, the tools available for making this decision are limited, highly subjective, time-consuming, and often extremely expensive. Therefore,embryologists use their experience, observation skills, and gut feeling to choose theembryothat is most likely to be successful. To change the routine and makein-vitro fertilizationprocess more accurate, scientists are seeking help fromartificial intelligence. The technology assists embryologists to make a consistent choice.Artificial intelligencesystem could learn how embryos develop over time and then uses the information to select the best embryos. The trained AIalgorithmcan find the successfulembryojust by looking at its image.

As more and more companies and medical institutions are coming forward to try their hand on thisartificial intelligenceespoused in IVF, we take you through some of the recent significant developments in the field.

Embryonics use AI to identify the most successful embryo: Embryonics, an Israeli AI fertility company has usedartificial intelligenceto increase the fertility rate and avoid the odds of successful implantation of theembryo. At the company, a group ofalgorithmspecialists, data scientists, and embryologists are developing analgorithmthat could predict theembryoimplanting probability. They have trained thealgorithmto analyze IVF time-lapsing imaging of developing embryos. The team is using medical imaging withdeep learningto curate datasets from tens of thousands of IVF cycles, including time-lapse videos of embryos. Embryonics is planning to streamline this fertility process by conducting clinical trials at several sites in the United States after obtaining the US Food and Drug Administrations approval.

Austin Fertility Center seeks AIs help for embryo analysis: Austin Fertility Center in the United States is also usingartificial intelligenceto non-invasively analyze embryos and determine whether they are euploid or aneuploid. The center has successfully applied AI inembryoselection with the help ofdeep learningthrough computer vision. They use 2D statistic images of embryos created through past IVF cycles at Ovation Fertility IVF laboratories to train thealgorithm. Austin Fertility Center also said that the method has shown 32% improvement in the prediction of successful implantation.

VIOLET, a tool that beats human analysis in embryo selection: Scientists from CARE Fertility, one of the leading independent providers of fertility treatment in the United Kingdom has joined hands with Canadian med-tech partner Future Fertility onembryoanalysis. The duo has researched to know howartificial intelligencecan be used as a more accurate tool to predict human egg fertilization andembryodevelopment. Recently, they also launched VIOLET, an AIalgorithmthat has outperformed human analysis, predicting human egg fertilization and blastocyst embryo development with 77% and 62% accuracy respectively.

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Why Artificial Intelligence is the Magic Tool in Fertility Treatment? - Analytics Insight

Artificial intelligence and machine learning have emerged as important tools for modernizing systems and simplifying federal processes. Nevertheless, they require the right data for training the algorithms.

Humans train algorithms by using them and over time, algorithms learn via a deep neural network.

Chris Ritter, leader of the Digital and Software Engineering group at the Idaho National Laboratory, said that while the ultimate goal of artificial intelligence is to get a computer to think like humans or surpass humans in terms of predicting functionality, machine learning is about having pre-programmed devices, which can conduct analysis on their own. Scaling up general artificial intelligence, from something like a simple Google CAPTCHA form to operating a nuclear reactor, is what his office looks into.

Where a lot of the existing research is, is in getting the data curated, and getting the data in a format thats possible to get those scale-up advantages, and to apply machine learning to some of our complex problems in the energy domain, Ritter said on Federal Monthly Insights Artificial Intelligence and Data.

Aside from deep neural networks, which are a kind of black box not easily audited, Ritter said another kind of algorithm is called explainable or transparent artificial intelligence.

What that means is, its mathematical. Right? So its completely auditable. And we can apply some penalize regression techniques to those areas, and you can make that a more novel technique, he said on Federal Drive with Tom Temin. And what a lot of people dont think about is, if you have a ton of data image recognition is a great example, right? Then DNN these deep neural networks are a great approach. But if you have less data, sometimes its better to apply a common statistical approach.

In use cases such as life safety and critical safety systems, its important to be able to audit what the algorithm will do and why that is.

At the Idaho National Laboratory, Ritter engages in digital engineering which uses key tenets of modeling, building from a source of truth, and innovation to name a few. The group has tried to change the way people work and have them produce data into buckets that engineers can already mine. Ritter said theyre trying this approach rather than seeing how they can make an algorithm smarter. Lets make the humans change their pattern a little bit.

On the innovation front, he cited the Versatile Test Reactor project as an example. The reactor is being built to performing irradiation testing at higher neutron energy fluxes than what is currently available, and as a result could help accelerate testing of advanced nuclear fuels, materials, instrumentation, and sensors, according to the Energy Department. Ritter said a lot of university researchers were incorporated into the project, who bring novel AI techniques to the table.

To ensure digital engineering of these massive projects at the laboratory produce usable, real-world results, engineers build ontologies, or blueprints, for the data to curate it. Examples of data could be equipment lists, computer-aided design files, costs, schedule information, risks and data from plant operators, Ritter said. When these subsystems are generating so much more data than anyone can possibly look at in an hour, predictive maintenance can spot anomalies and raise a red flag.

And so in other applications and other industries were seeing predictive maintenance applied. And so we know that that technique is certainly possible, in the design side being able to apply artificial intelligence during the design of an asset, he said. I think we are still in the early stages of that idea.

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Idaho National Lab's digital engineering team relies on algorithms and auditable data - Federal News Network

TORONTO, May 5, 2021 /PRNewswire-PRWeb/ -- Smart manufacturing the use of advanced automation and big data technologies to detect and predict anomalies and optimize yield and other outcomes is a business imperative in today's highly-competitive, highly-regulated pharmaceutical industry. But how can established pharma and biotech companies intelligently implement smart manufacturing strategies that leverage artificial intelligence (AI), machine learning (ML), Internet of Things (IoT) sensors, cloud infrastructure and rich big data analytics tools into their commercial manufacturing processes without disrupting existing workflows? Validated SOPs, GxP compliance, friction with existing tools and workflows, and organizational inertia make change disruptive for the organization and ripe for risk.

Join expert speakers from Aizon, Lawrence Baisch, Chief Customer Success Officer and Kevin Baughman, Data Science Practice Lead, in a live webinar on Tuesday, May 25, 2021 at 1pm EDT to hear about how to keep ROI in focus and identify value-based use cases for smart manufacturing initiatives for pharma and biotech, as well as how to avoid prevalent pitfalls when implementing smart manufacturing strategies and technologies in your commercial manufacturing processes.

For more information, or to register for this event, visit Artificial Intelligence (AI) in Pharma and Identifying Value-Driven Use Cases for Smart Manufacturing Initiatives.

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