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Applications of AI And Machine Learning In Computer Science and Electrical Engineering

Technologically, we are evolving with every passing day. Progress in the field of Artificial intelligence and machine learning has transformed our lives for the better. Today, these magnificent technologies are used to optimize systems and meet the desired organizations goals. AI and machine learning not only boost the performance of the system but also address the problems of the business like never before. Additionally, problems are addressed efficiently and faster than before. All in all, implementing the latest applications of AI and machine learning might end up being a path for achieving greater heights. Computer engineering systems and electrical engineering systems generate huge volumes of data. Thus, we can apply data mining to discover new relationships in these systems. With the advent of deep neural networks thanks to the advancement in technology, we can learn new mappings between inputs and output of these systems. On that note, have a look at some of the greatest applications of AI and machine learning in the field of Computer engineering and electrical engineering that have simplified our lives.

Power systems

One of the best applications of AI when it comes to computer engineering has been on power systems. Right from identifying malfunctions to forecasting, AI has covered it all. Artificial intelligence has done a magnificent job in reducing the workload of human operators by taking up tasks such as data processing, routine maintenance, training, etc.

Application of Artificial intelligence in Electrical Equipment

First things first, we all know how complex the electrical equipment structure is. In reality, it not only needs knowledge pertaining to electronics, circuits, electromagnetic fields, motors, automation, etc. but also the necessity to understand the generators, sensors and other components of the role and mechanism. It is here that AI turns out to be no less than a saviour. Through programming and operation by computer technology, AI can realize the automatic operation of electrical equipment and replace human labour as well, thereby reducing the labour cost to a large extent. Additionally, Artificial intelligence technology greatly improves the speed and precision of the work.

Fault diagnosis

Artificial intelligence can be used in the logic of fuzzy neural network expert systems timely. With this, it is not only possible to accurately detect the faults, but also used to determine the cause of the failure, type and location of thefailure, and timely control of fault repair.

More secure systems

With the help of advanced search algorithms, Artificial intelligence and machine learning, identifying potential threats and data breaches in real-time has become easier than ever. Well, this is not it there is more to this. These advanced technologies also provide the necessary solutions to avoid those issues in the future. Well, there is no denying that when it comes to computer science, data security becomes way more relevant, right?

Server optimization

We all know that hosting servers have millions of inbound requests on a day-to-day basis. However, a point of concern is that due to the continuous flow of queries, some of these servers may end up slowing down and become unresponsive. Well, Artificial intelligence to the rescue it is! AI holds the potential of optimizing the host server and enhancing the operations, thereby boosting customer service.

What everything boils down to is the fact that AI and machine learning are changing many sectors, particularly IT/computer and electrical engineering because of the amount of data sets it can process at greater speeds and ability to learn faster than the human brain.

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Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

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Applications of AI And Machine learning In Computer Science and Electrical Engineering - Analytics Insight

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Women in the AI field are making research breakthroughs, spearheading vital ethical discussions, and inspiring the next generation of AI professionals. We created the VentureBeat Women in AI Awards to emphasize the importance of their voices, work, and experience and to shine a light on some of these leaders. In this series, publishing Fridays, were diving deeper into conversations with this years winners, whom we honored recently at Transform 2021. Check out last weeks interview with a winner of our AI rising star award.

No one got more nominations for a VentureBeat AI award this year than Katia Walsh, a reflection of her career-long effort to mentor women in AI and data science across the globe.

For example, Mark Minevich, chair of AI Policy at International Research Center of AI under UNESCO, said, Katia is an impressive, values-driven leader [who has] been a diversity champion and mentor of women, LGBTQ, and youth at Levi Strauss & Co, Vodafone, Prudential, Fidelity, Forrester, and in academia over many years. And Inna Saboshchuk, a current colleague of Walshs at Levi Strauss & Co, said, a single conversation with her will show you how much she cares for the people around her, especially young professionals within AI.

In particular, these nominators and many others highlighted Walshs efforts to upskill team members. Most recently, she launched a machine learning bootcamp that allowed people with no prior experience to not only learn the skills, but apply them every day in their current roles.

VentureBeat is thrilled to present Walsh with this much-deserved AI mentorship award. We recently caught up with her to learn more about the early success of her latest bootcamp, the power of everyday mentorship, and the role it can play in humanizing AI.

This interview has been edited for brevity and clarity.

VentureBeat: You received a ton of nominations for this award, so clearly youre making a real impact. How would you describe your approach to AI mentorship?

Katia Walsh: My approach is not specific to AI mentorship, but rather overall leadership. I consider myself to be a servant leader, and I see my job as serving the people on my teams, my partners teams, and at the companies that I have the privilege to work for. My job is to remove barriers to help them grow, learn, engage, and mobilize others to succeed. So that extends to AI, but its not limited to that alone.

VentureBeat: Can you tell us about some of the specific initiatives youve launched? I know at Levi Strauss & Co, for example, you recently created a machine learning bootcamp to train more than 100 employees who had no prior machine learning experience, most of them women. Thats amazing.

Walsh: Absolutely. So we are still in the process. We just started our first cohort between April and May, where we took people with absolutely no experience in coding or statistics from all areas of the company including warehouses, distribution centers, and retail stores and sought to make sure we gave people across geographies and across the company the opportunity to learn machine learning and practice that in their day job, regardless of what that day job was.

So we trained the first cohort with 43 people, 63% of whom were women in 14 different locations around the world. And thats very important because diversity comes in so many different ways, including cultural and geographic diversity. And so that was very successful; every single one of those employees completed the bootcamp. And now were about to start our second cohort with 60 people, which will start in September and complete in November.

VentureBeat: Im glad you mentioned those different aspects of diversity, because the industry is full of conversations around diversity, inclusion efforts, and ethical AI some of them more genuine than others. So how does AI mentorship ladder up to all that?

Walsh: I see it as just yet another platform to make an impact. AI is such an exciting field, but it can also be seen as intimidating. Some people dont know if its technology or business, but the answer is both. In fact, AI is actually part of our personal lives as well. One of my goals is to humanize the field of AI so that everyone understands the benefits and feels the freedom and the power to contribute to it. And by feeling that, they will in turn help make it even more diverse. At the end of the day at this point, at least AI is the product of human beings, with all of human beings mindsets, capabilities, and limitations. And so, its also imperative to ensure that when we create algorithms, use data, and deliver digital products, we do our very best to really reflect the world we live in.

VentureBeat: We talked about initiatives, but of course mentorship is also about those everyday mentorship-like interactions, such as with ones manager or an industry connection. How important are these not just for personal development, but also running a business and being part of a team?

Walsh: Thats actually probably the most important stage. Our daily lives revolve around what might be considered the mundane meetings, tasks, assignments, deadlines and thats actually where we can make the most impact. Mentorship is really not about doing something special and extra, but rather making sure that as part of our daily lives and daily responsibilities and jobs, we ensure we think about if were being equitable, fair, and doing everything we can to bring diversity. But it cant be a box to check; it has to become a part of how we think and act every hour in every single day.

VentureBeat: Are there any misconceptions about mentorship you think are important to clear up, or often overlooked aspects of mentorship you think everyone should know about?

Walsh: One thing that comes to mind is this idea that women can only be mentored by other women. Thats actually not the case. And in my own experience, Ive had the great privilege of working with men who have themselves taken the chance on me, given me opportunities, and given me responsibilities even before I felt ready. And I really appreciate that. So everyone can be a mentor to women and all genders including fluid genders regardless of their own gender, job, or role.

VentureBeat: And do you have any advice for everyone, but especially business leaders, about how they can be better mentors? Or what about advice for people looking to be mentored about how to make the most out of those relationships and everyday interactions?

Walsh: Ill address the mentee question first. Ive really been impressed with people who, even at a very young age, have had the courage, incentive, and initiative to reach out and say, I want to learn from you. Can you spend a few minutes with me? I always take the call. So I really encourage people to feel that strength and to take that initiative to reach out to people they think they can learn from. And I encourage those who are mentors to also take that call and to proactively encourage others to stay connected with them. One of the things I did was actually give my cell phone number to everyone in my company. Its not commonly done, but Ive put it in our own town hall chat because I want people to feel that connection. I dont want anyone to feel intimidated by a title or where someone sits in a company. AI, data, and digital are truly transversal. Theyre horizontal and cut across everything in a company. So its part of what I do in my function, but its also part of really wanting to contribute to diversity and mentorship.

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How to upskill your team to tackle AI and machine learning - VentureBeat

Meet Taktile, a new startup that is working on a machine learning platform for financial services companies. This isnt the first company that wants to leverage machine learning for financial products. But Taktile wants to differentiate itself from competitors by making it way easier to get started and switch to AI-powered models.

A few years ago, when you could read machine learning and artificial intelligence in every single pitch deck, some startups chose to focus on the financial industry in particular. It makes sense as banks and insurance companies gather a ton of data and know a lot of information about their customers. They could use that data to train new models and roll out machine learning applications.

New fintech companies put together their own in-house data science team and started working on machine learning for their own products. Companies like Younited Credit and October use predictive risk tools to make better lending decisions. They have developed their own models and they can see that their models work well when they run them on past data.

But what about legacy players in the financial industry? A few startups have worked on products that can be integrated in existing banking infrastructure. You can use artificial intelligence to identify fraudulent transactions, predict creditworthiness, detect fraud in insurance claims, etc.

Some of them have been thriving, such as Shift Technology with a focus on insurance in particular. But a lot of startups build proof of concepts and stop there. Theres no meaningful, long-term business contract down the road.

Taktile wants to overcome that obstacle by building a machine learning product that is easy to adopt. It has raised a $4.7 million seed round led by Index Ventures with Y Combinator, firstminute Capital, Plug and Play Ventures and several business angels also participating.

The product works with both off-the-shelf models and customer-built models. Customers can customize those models depending on their needs. Models are deployed and maintained by Taktiles engine. It can run in a customers cloud environment or as a SaaS application.

After that, you can leverage Taktiles insights using API calls. It works pretty much like integrating any third-party service in your product. The company tried to provide as much transparency as possible with explanations for each automated decision and detailed logs. As for data sources, Taktile supports data warehouses and data lakes as well as ERP and CRM systems.

Its still early days for the startup, and its going to be interesting to see whether Taktiles vision pans out. But the company has already managed to convince some experienced backers. So lets keep an eye on them.

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Taktile makes it easier to leverage machine learning in the financial industry - TechCrunch

As recent events have shown, military decision-making is one of the highest-stakes challenges in the world: Diplomatic relations are at stake; billions of dollars of tax-funded budgets are in the balance; the safety and well-being of thousands of military and civilian personnel around the globe are on the line; and above all, the freedom and liberty of the United States and its more than 330 million citizens must be protected. But with such immense stakes comes an almost unfathomably large amount of related data that must be taken into account. Whether it is managing population health in an increasingly complex and connected world, or managing decisions on the network-centric battlefield, standalone humans are proving insufficient to harness the data, analyze it, and make timely and correct decisions.

Spanning six branches and upward of 1.3 million active duty military personnel on all seven continents, how can all of the data points from dictates from the commander-in-chief to handwritten notes on the deck of an aircraft carrier be taken into account? In matters of national security, speed and reliability in decision-making and avoiding technological surprises or being caught off guard by the nations political rivals require massive real-time analysis and first and second order thinking that includes the complexities of human behavior.

Consider all of the stakes and moving parts facing the leadership at a large domestic military base during the recent COVID-19 pandemic. Concerns of COVID-19 did not just need to consider the base personnel, but also the behavior of the civilians in the surrounding counties, as people from throughout the region, military and civilian contractors alike, were coming and going daily. The information necessary to consider starts with infection and hospitalization rates, but also includes behavior monitoring (and influencing) as well as staying up to date with steps being taken by local, regional and state officials to monitor the virus and limit its spread. With so many moving parts, it is very difficult to stay up to the minute on everything and to determine the right decision with any degree of certainty.

The answer to this guesswork and analysis paralysis lies in the capabilities of artificial intelligence and machine learning. If the military continues to waste too much time with human hours of effort and analysis that could be handled by machines, that could lead to danger and even death of military personnel or civilians. At the heart of complex systems, such as the U.S. military, there is a critical tipping point where the systems are so complex that humans can no longer track them. But AI solutions are capable of delivering up-to-the-minute data modeling, considering all factors at play and second and third order consequences, that can present tangible, data-driven intelligence that takes actions far beyond the limitations of linear human minds. Perhaps the biggest benefit is the confidence to avoid the negative publicity from the podium moment, when asked to justify decisions. Decision-makers can confidently move beyond relying on hunches and instead identify data based on sub-indexes, models from experts, and simulations specific to that day and the circumstances specific to each facility.

When President Biden was recently called onto the carpet to explain the rapid fall of Afghanistan in nine days, he should have had an AI that could at least explain the data, the models and weights that fed the analysis, conclusions and decisions based on the belief that the 300,000 strong Afghan army would be able to hold off the 60,000 Taliban fighters long enough for an orderly withdrawal. Journalists would then be free to question the data sources, the models or the weightings, but not the president, who would be relying on these systems for his judgment. But more importantly, such a system would have certainly predicted this rapid fall in its Monte Carlo distribution of potential outcomes, and would have generated counter measures and cautions.

Without a deeper commitment to AI, the military risks missing out on intelligence that transcends classified, siloed and otherwise restricted information without compromising security. One of the biggest challenges to high-stakes decision-making in the military is silos of classified information, making it difficult or impossible for every party to know every factor that is shaping the situation.

Using AI and machine learning solves this challenge safely. Rather than dumping disparate data from various branches of the military and clearance level into one gigantic data lake, it is possible to leave all the data safely and securely where it is, and train a machine to know and inform the human decision-makers that the data exists. AI is capable of processing not only all of the information in the corpus, but it is also able to know which parties do and do not have clearance to each individual piece of data. In matters of classified information, it can tell different personnel that the information exists, and direct these individuals to the authority qualified to disclose it.

Capabilities like these can be readily applied to large, complex military undertakings, featuring processes, decisions and volumes of information. For instance, when a new aircraft carrier is being built, management requires information in hand-written reports. It is difficult for the naked eye to tell if the project is on time or on budget because of the heavy reliance on human judgment. If any human assessment is just a fraction off, it can massively impact the whole project.

Recent challenges that factor in the vagaries of human behavior illustrated starkly by COVID-19 and the withdrawal from Afghanistan, beg for the rapid analysis and creative input of machine learning systems. From digestion and quantification of countless data points to absorbing and cataloging knowledge of experts who will not always be around to help with predictive modeling of circumstances with dozens of variables, this amplified intelligence is the key to better outcomes.

Richard Boyd is CEO at Tanjo, a machine learning company.

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COVID-19 showed why the military must do more to accelerate machine learning for its toughest challenges - C4ISRNet

Climate change is affecting farming all over the world, and solutions are seldom simple. But if you could plant crops that resisted the heat, cold or drought instead of moving a thousand miles away, wouldnt you? Avalo helps plants like these become a reality using AI-powered genome analysis that can reduce the time and money it takes to breed hardier plants for this hot century.

Founded by two friends who thought theyd take a shot at a startup before committing to a life of academia, Avalo has a very direct value proposition, but it takes a bit of science to understand it.

Big seed and agriculture companies put a lot of work into creating better versions of major crops. By making corn or rice ever so slightly more resistant to heat, insects, drought or flooding, they can make huge improvements to yields and profits for farmers, or alternatively make a plant viable to grow somewhere it couldnt before.

There are big decreases to yields in equatorial areas and its not that corn kernels are getting smaller, said co-founder and CEO Brendan Collins. Farmers move upland because salt water intrusion is disrupting fields, but they run into early spring frosts that kill their seedlings. Or they need rust resistant wheat to survive fungal outbreaks in humid, wet summers. We need to create new varieties if we want to adapt to this new environmental reality.

To make those improvements in a systematic way, researchers emphasize existing traits in the plant; this isnt about splicing in a new gene but bringing out qualities that are already there. This used to be done by the simple method of growing several plants, comparing them, and planting the seeds of the one that best exemplifies the trait like Mendel in Genetics 101.

Nowadays, however, we have sequenced the genome of these plants and can be a little more direct. By finding out which genes are active in the plants with a desired trait, better expression of those genes can be targeted for future generations. The problem is that doing this still takes a long time as in a decade.

The difficult part of the modern process stems (so to speak) from the issue that traits, like survival in the face of a drought, arent just single genes. They may be any number of genes interacting in a complex way. Just as theres no single gene for becoming an Olympic gymnast, there isnt one for becoming drought-resistant rice. So when the companies do what are called genome-wide association studies, they end up with hundreds of candidates for genes that contribute to the trait, and then must laboriously test various combinations of these in living plants, which even at industrial rates and scales takes years to do.

Numbered, genetically differentiated rice plants being raised for testing purposes. Image Credits: Avalo

The ability to just find genes and then do something with them is actually pretty limited as these traits become more complicated, said Mariano Alvarez, co-founder and CSO of Avalo. Trying to increase the efficiency of an enzyme is easy, you just go in with CRISPR and edit it but increasing yield in corn, there are thousands, maybe millions of genes contributing to that. If youre a big strategic [e.g., Monsanto] trying to make drought-tolerant rice, youre looking at 15 years, 200 million dollars its a long play.

This is where Avalo steps in. The company has built a model for simulating the effects of changes to a plants genome, which they claim can reduce that 15-year lead time to two or three and the cost by a similar ratio.

The idea was to create a much more realistic model for the genome thats more evolutionarily aware, said Collins. That is, a system that models the genome and genes on it that includes more context from biology and evolution. With a better model, you get far fewer false positives on genes associated with a trait, because it rules out far more as noise, unrelated genes, minor contributors and so on.

He gave the example of a cold-tolerant rice strain that one company was working on. A genomewide association study found 566 genes of interest, and to investigate each costs somewhere in the neighborhood of $40,000 due to the time, staff and materials required. That means investigating this one trait might run up a $20 million tab over several years, which naturally limits both the parties who can even attempt such an operation, and the crops that they will invest the time and money in. If you expect a return on investment, you cant spend that kind of cash improving a niche crop for an outlier market.

Were here to democratize that process, said Collins. In that same body of data relating to cold-tolerant rice, We found 32 genes of interest, and based on our simulations and retrospective studies, we know that all of those are truly causal. And we were able to grow 10 knockouts to validate them, three in a three-month period.

In each graph, dots represent confidence levels in genes that must be tested. The Avalo model clears up the data and selects only the most promising ones. Image Credits: Avalo

To unpack the jargon a little there, from the start Avalos system ruled out more than 90% of the genes that would have had to be individually investigated. They had high confidence that these 32 genes were not just related, but causal having a real effect on the trait. And this was borne out with brief knockout studies, where a particular gene is blocked and the effect of that studied. Avalo calls its method gene discovery via informationless perturbations, or GDIP.

Part of it is the inherent facility of machine learning algorithms when it comes to pulling signal out of noise, but Collins noted that they needed to come at the problem with a fresh approach, letting the model learn the structures and relationships on its own. And it was also important to them that the model be explainable that is, that its results dont just appear out of a black box but have some kind of justification.

This latter issue is a tough one, but they achieved it by systematically swapping out genes of interest in repeated simulations with what amount to dummy versions, which dont disrupt the trait but do help the model learn what each gene is contributing.

Avalo co-founders Mariano Alvarez (left) and Brendan Collins by a greenhouse. Image Credits: Avalo

Using our tech, we can come up with a minimal predictive breeding set for traits of interest. You can design the perfect genotype in silico [i.e., in simulation] and then do intensive breeding and watch for that genotype, said Collins. And the cost is low enough that it can be done by smaller outfits or with less popular crops, or for traits that are outside possibilities since climate change is so unpredictable, who can say whether heat- or cold-tolerant wheat would be better 20 years from now?

By reducing the capital cost of undertaking this exercise, we sort of unlock this space where its economically viable to work on a climate-tolerant trait, said Alvarez.

Avalo is partnering with several universities to accelerate the creation of other resilient and sustainable plants that might never have seen the light of day otherwise. These research groups have tons of data but not a lot of resources, making them excellent candidates to demonstrate the companys capabilities.

The university partnerships will also establish that the system works for fairly undomesticated plants that need some work before they can be used at scale. For instance it might be better to supersize a wild grain thats naturally resistant to drought instead of trying to add drought resistance to a naturally large grain species, but no one was willing to spend $20 million to find out.

On the commercial side, they plan to offer the data handling service first, one of many startups offering big cost and time savings to slower, more established companies in spaces like agriculture and pharmaceuticals. With luck Avalo will be able to help bring a few of these plants into agriculture and become a seed provider as well.

The company just emerged from the IndieBio accelerator a few weeks ago and has already secured $3 million in seed funding to continue their work at greater scale. The round was co-led by Better Ventures and Giant Ventures, with At One Ventures, Climate Capital, David Rowan and of course IndieBio parent SOSV participating.

Brendan convinced me that starting a startup would be way more fun and interesting than applying for faculty jobs, said Alvarez. And he was totally right.

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Avalo uses machine learning to accelerate the adaptation of crops to climate change - TechCrunch