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Companies everywhere are pouring resources into artificial intelligence (A.I.) and machine learning (ML) initiatives. Many technologists believe that apps smartened with A.I. and ML tools will eventually offer better customer personalization; managers hope that A.I. will lead to better data analysis, which in turn will power better business strategies.

But which industries are actually hiring A.I. specialists? If you answer that question, it might give you a better idea of where those resources are being deployed. Fortunately,CompTIAs latest Tech Jobs Reportoffers a breakdown of A.I. hiring, using data from Burning Glass, which collects and analyzes millions of job postings from across the country. Check it out:

Perhaps its no surprise that manufacturing tops this list; after all, manufacturers have been steadily automating their production processes for years, and it stands to reason that they would turn to A.I. and ML to streamline things even more. In theory, A.I. will also help manufacturers do everythingfrom reducing downtime to improving supply chainsalthough it may take some time to get the models right.

The presence of healthcare, banking, and public administration likewise seem logical.These three industries have the money to invest in A.I. and ML right now and have the greatest opportunity to see the investment pay off, fast, Gus Walker, director of product at Veritone, an A.I. tech company based in Costa Mesa, California,told Dicelate last year.That being said, the pandemic has caused industries hit the hardest to take a step back and look at how they can leverage AI and ML to rebuild or adjust in the new normal.

Compared to overall tech hiring, the number of A.I.-related job postings is still relatively small. Right now, mastering and deploying A.I. and machine learning is something of a specialist industry; but as these technologies become more commodified, and companies develop tools that allow more employees to integrate A.I. and ML into their projects, the number of job postings for A.I. and ML positions could increase over the next several years. Indeed, one IDC report from 2020 found three-quarters of commercial enterprise applications could lean on A.I. in some way by2021.

Its also worth examining where all that A.I. hiring is taking place; its interesting that Washington DC tops this particular list, with New York City a close second; Silicon Valley and Seattle, the nations other big tech hubs, are somewhat further behind, at least for the moment. Washington DC is notable not only for federal government hiring, but the growing presence of companies such as Amazon that hunger for talent skilled in artificial intelligence:

Jobs that leverage artificial intelligence are potentially lucrative, with a current median salary (according to Burning Glass)of $105,000. Its also a skill-set thatmore technologists may need to become familiar with, especially managers and executives.A.I. is not going to replace managers but managers that use A.I. will replace those that do not, Rob Thomas, senior vice president of IBMscloudand data platform,recently told CNBC. If you mention A.I. or ML on your resume and applications, make sure you know your stuff before the job interview; chances are good youll be tested on it.

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Which Industries are Hiring AI and Machine Learning Roles? - Dice Insights

AUSTIN, Texas Our homes and offices are only as solid as the ground beneath them. When that solid ground turns to liquid as sometimes happens during earthquakes it can topple buildings and bridges. This phenomenon is known as liquefaction, and it was a major feature of the 2011 earthquake in Christchurch, New Zealand, a magnitude 6.3 quake that killed 185 people and destroyed thousands of homes.

An upside of the Christchurch quake was that it was one of the most well-documented in history. Because New Zealand is seismically active, the city had numerous sensors for monitoring earthquakes. Post-event reconnaissance provided a wealth of additional data on how the soil responded across the city.

Two researchers from The University of Texas at Austin developed a machine learning model that predicted the amount of lateral movement that occurred when the Christchurch earthquake caused soil to lose its strength and shift relative to its surroundings.

The results were published online in Earthquake Spectra in April 2021.

Its one of the first machine learning studies in our area of geotechnical engineering, said postdoctoral researcher Maria Giovanna Durante, a Marie Sklodowska Curie fellow previously at UT Austin. Its an enormous amount of data for our field. If we have thousands of data points, maybe we can find a trend.

Durante and Ellen Rathje, the Janet S. Cockrell Centennial Chair in Engineering at UT Austin and the principal investigator for the National Science Foundation-funded DesignSafe cyberinfrastructure, first used a Random Forest approach with a binary classification to forecast whether lateral spreading movements occurred at a specific location. They then applied a multiclass classification approach to predict the amount of displacement, from none to more than 1 meter.

It was important to select specific input features that go with the phenomenon we study, Durante said. Were not using the model as a black box were trying to integrate our scientific knowledge as much as possible.

Durante and Rathje trained the model using data related to the peak ground shaking experienced (a trigger for liquefaction), the depth of the water table, the topographic slope, and other factors. In total, more than 7,000 data points from a small area of the city were used for training data a great improvement, as previous geotechnical machine learning studies had used only 200 data points.

They tested their model citywide on 2.5 million sites around the epicenter of the earthquake to determine the displacement. Their model predicted whether liquefaction occurred with 80% accuracy; it was 70% accurate at determining the amount of displacement.

The researchers used the Frontera supercomputer at the Texas Advanced Computing Center (TACC), one of the worlds fastest, to train and test the model. TACC is a key partner on the DesignSafe project, providing computing resources, software and storage to the natural hazards engineering community.

Access to Frontera provided Durante and Rathje machine learning capabilities on a scale previously unavailable to the field. Deriving the final machine learning model required testing 2,400 possible models.

It would have taken years to do this research anywhere else, Durante said. If you want to run a parametric study or do a comprehensive analysis, you need to have computational power.

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New Type of Machine Learning Aids Earthquake Risk Prediction - UT News - UT News | The University of Texas at Austin

As machines increasingly make high-risk decisions, a new lab at Texas A&M aims to reduce bias in artificial intelligence and machine learning.

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The algorithms underpinning artificial intelligence and machine learning increasingly influence our daily lives. They can decide everything from which video were recommended to watch next on YouTube to who should be arrested based on facial recognition software.

But the data used to train these systems often replicate the harmful social biases of the engineers who build them. Eliminating this bias from technology is the focus of Code^Shift, a new data science lab at Texas A&M University that brings together faculty members and researchers from a variety of disciplines across campus.

Its an increasingly critical initiative, said Lab Director Srividya Ramasubramanian, as more of the world becomes automated. Machines, rather than humans, are making many of the decisions around us, including some that are high-risk.

Code^Shift tries to shift our thinking about the world of code or coding in terms of how we can be thinking of data more broadly in terms of equity, social healing, inclusive futures and transformation, said Ramasubramanian, professor of communication in the College of Liberal Arts. A lot of trauma and a lot of violence has been caused, including by media and technologies, and first we need to acknowledge that, and then work toward reparations and a space of healing individually and collectively.

Bias in artificial intelligence can have major impacts. In just one recent example, a man has sued the Detroit Police Department after he was arrested and jailed for shoplifting after being falsely identified by the departments facial recognition technology. The American Civil Liberties Union calls it the first case of its kind in the United States.

Code^Shift will attempt to confront this issue using a collaborative research model that includes Texas A&M experts in social science, data science, engineering and several other disciplines. Ramasubramanian said eight different colleges are represented, and more than 100 people attended the labs virtual launch last month.

Experts will work together on research, grant proposals and raising awareness in the broader public of the issue of bias in machine learning and artificial intelligence. Curriculum may also be developed to educate professionals in the tech industry, such as workshops and short courses on anti-racism literacy, gender studies and other topics that are sometimes not covered in STEM fields.

The labs name references coding, which is foundational to todays digital world. Its also a play on code-switching the way people change the languages they use or how they express themselves in conversation depending on the context.

As an immigrant, Ramasubramanian says shes familiar with living in two worlds. She offers several examples of computer-based biases shes encountered in everyday life, including an experience attempting to wash her hands in an airport bathroom.

Standing at the sink, Ramasubramanian recalls, she held her hands under the faucet. As she moved them back and forth and the taps stayed dry, she realized that the sensors used to turn the water on could not recognize her hands. It was the same case with the soap dispenser.

It was something I never thought much about, but later on I was reading an article about this topic that said many people with darker skin tones were not recognized by many systems, she said.

Similarly, when Ramasubramanian began to work remotely during the COVID-19 pandemic, she noticed that her skin and hair color made her disappear against the virtual Zoom backgrounds. Voice recognition software she attempted to use for dictation could not understand her accent.

The system is treating me as the other and different in many, many ways, she said. And in return, there are serious consequences of who feels excluded, and thats not being captured.

Co-director Lu Tang, an assistant professor in the College of Liberal Arts who examines health disparity in underserved populations, says her research shows that Black patients, for example, must have much more severe symptoms that non-Black patients in order to be assigned certain diagnoses in computer software used in hospitals.

She said this is just one instance of the disparities embedded in technology. Tangs research also focuses on how machine learning algorithms used on social media platforms are more likely to expose people to misinformation about health.

If I inhabit a social media space where a lot of my friends hold certain erroneous attitudes about things like vaccines or COVID-19, I will repeatedly be exposed to the same information without being exposed to different information, she said.

Tang also is interested in what she calls the filter bubble the phenomenon of where an algorithm leads a user on TikTok, YouTube or other platforms based on content theyve watched in the past or what other people with similar viewing behaviors are watching at that moment. Watching just one video containing vaccine misinformation could prompt the algorithm to continue recommending similar videos. Tang said the filter bubble is another added layer that influences the content that people are exposed to.

I think to really understand this society and how we are living today, we as social scientists and humanities scholars need to acknowledge and understand the way computers are influencing the way society is run today, Tang said. I feel like working with computer science engineers is a way for us to combine our strengths to understand a lot of the problems we have in this society.

Computer Science and Engineering Assistant Professor Theodora Chaspari, another co-director of Code^Shift, agrees that minds from different disciplines are needed to design better systems.

To build an inclusive system, she said, engineers need to include representative data from all populations and social groups. This could help facial recognition algorithms better recognize faces of all races, she said, because a system cannot really identify a face until it has seen many, many faces. But engineers may not understand more subtle sources of bias, she said, which is why social and life sciences experts are needed to help with the thoughtful design of more equitable algorithms.

The goal of Code^Shift is to help bridge the gap between systems and people, Chaspari said. The lab will do this by raising awareness through not only research, but education.

Were trying to teach our students about fairness and bias in engineering and artificial intelligence, Chaspari said. Theyre pretty new concepts, but are very important for the new, young engineers who will come in the next years.

So far, Code^Shift has held small group discussion on topics like climate justice, patient justice, gender equity and LGBTQ issues. A recent workshop focused on health equity and the ways in which big data and machine learning can be used to take into account social structures and inequalities.

Ramasubramanian said a full grant proposal to the Texas A&M Institute of Data Science Thematic Data Science Labs Program is also being developed. The labs directors hope to connect with more colleges and make information accessible to more people.

They say collaboration is critical to the initiative. The people who create algorithms often come from small groups, Ramasubramanian said, and are not necessarily collaborating with social scientists. Code^Shift asks for more accountability in how systems are created: who has access to the data, whos deciding how to use it, and how is it being shared?

Texas A&M is home to some of the worlds top data scientists, Ramasubramanian said, making it an important place to have conversations about difficult topics like data equity.

To me, we should also be leaders in thinking about the ethical, social, health and other impacts of data, she said.

To join the Code^Shift mailing list or learn more about collaborating with the lab, contact Ramasubramanian at srivi@tamu.edu.

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Code^Shift Lab Aims To Confront Bias In AI, Machine Learning - Texas A&M Today - Texas A&M University Today

Jonathan Jadali, Founder and CEO of Ascend

The jury is still out on what makes Gen Zers tick, but while the research is still ongoing there is much evidence to suggest that a marketing strategy utilizing machine learning is exponentially more effective with the next generation.

One thing is abundantly clear to every marketer worth his salt; Gen Z customers are "ninja-level" efficient at swatting away regular ads and pop-ups. They are strongly immune to hard sales and obvious sales content.

Despite all the difficulties that marketers are facing in reaching a wide Gen Z audience, Jonathan Jadali, CEO and Founder at Ascend Agency has found great success in leading Gen Z-focused startups to victory in this marketing struggle.

So what makes the typical Gen Z customer tick and how can businesses and startups build a brand that is appealing to them, utilizing cutting edge technologies?

Jadali shares the ways in which he has used a data and machine-learning strategy in getting many of his clients from obscurity to domination of the Gen Z market.

Content, as they say, is king, but the wrong kind of content isnt even fit to be a pawn in this game. To get startups headed in the right direction, Jonathan often helps direct his clients at Ascend Agency on creating the right type of content for the right type of client.

While most brands are focused on putting out well-curated video and image content in a bid to drive engagement on their social media platforms, Jadali advises that this might not be the best way to go if Gen Zers are your target audience.

The ideal Gen Z customer thrives on spontaneous and messy content. As Jadali states, Gen Z customers are all about being realthey connect well with unfiltered and unedited content because it tends to feel less salesy than others.

For instance, a makeup brand is better off posting a video of a makeup session, in front of a cluttered vanity table, than a photoshoot with a perfectly made-up face.

This is important to keep in mind when implementing any machine learning into your marketing strategy. Whether you are creating a chat bot, or building a data-driven marketing campaign - its important that your system learns to be imperfect.

When AI or Machine Learning is used in marketing, sometimes it can come off as, well, robotic. Gen Z will be an important moment for machine learning marketing as it will help us get closer to contextual AI - machines that more accurately predict and reflect human behavior.

Gen Z wants to see the messiness of life and its process reflected in your content. Brands that do this, are the brands that they are drawn to and often build loyalty for.

How does it look? How effective is it? How satisfying is your service? All these are valid marketing questions and things that in the past had been asked by your millennial customer base.

According to Jadali, these questions do not matter nearly as much to a Gen Z audience.

Clearly, customers want products that work and businesses that deliver, but with a Gen Z audience, that doesnt seem to be the right way to lead in marketing to them.

Having worked with both Fortune 500 companies and smaller startups alike in the last 3 years since Ascend Agency launched, Jadali is fairly certain that Gen Z customers are way more attracted to how your business makes them feel.

This is where machine learning can really come in handy. Understanding your customers' moods and habits can help you tap into what makes them feel great about themselves and the products in their lives.

Gen Z customers are tired of hearing about how amazing your product is, businesses have been hyping up their products for as long as businesses have existed and Gen Zers arent having any more of it. In Jonathans words, Sell experiences, not products, and your products will head out of your door as well.

According to Mention, 25% of what you sell is your product. The additional 75% is the intangible feeling that comes with said product.

What dominant feeling do you want to evoke with your content? A question that is popularly asked at the Ascend Agency office, is one that has helped brands build consistency in their content style and delivery and that has brought the Gen Z customers in their droves.

This question can be answered through aggregated customer data that helps you better understand the emotions from brands that they also engage with.

Red Bull is a great example of a brand that utilizes data and machine learning in this manner. Their video content covers high-risk sports, like Skydiving, Bungee jumping, etc. From customer data processed by predictive analytics and machine learning systems, the dominant feeling Red Bull chose to evoke is one of courage and strength.

What is yours, Happiness, Reflection, or Prestige? The sooner you can answer that, the sooner you can get your gen Z audience to really pay attention. Machine learning can help you answer this question faster and more accurately.

Did you know that once an Influencers followership crosses the 100k mark, their engagement drops drastically? When did you last get an Instagram reply from Selena Gomez or Christiano Ronaldo? Never I presume. I will get back to this point in a bit.

While Guest Posting and proper ad placement might still work rather well for Millennials, Social media is clearly the major frontier for Gen Zers. This is why Influencer Marketing has risen to the fore in the last 6 years.

However, nothing is more important to this generation than being seen and heard. This is why Gen Z customers rate a brands authenticity by how well the brands engage with them online.

If a customer posts a tweet asking you for information or laying down a complaint, the first thing to do is to respond publicly before directing to their inbox as opposed to solely responding to them privately. If they send in a review, respond and thank them. Call them by name, engage with them personally in a way that doesnt feel rehearsed, says Jadali.

It goes without saying that brands should be more intentional with engaging their Gen Z audience personally. However, this is hard to scale.

Machine learning is helping brands go beyond the typical automated response we often see in DM and SMS replies. As this technology becomes more advanced, you will be able to engage with hundreds of thousands of customers at once at a deeply personal level.

Micro-influencers drive 60% higher engagement levels and 22.2% more weekly conversions coupled with the fact that they are considerably cheaper. However, their secret sauce is the fact that they are still able to engage with their followers directly far more than celebrities like Cristiano Ronaldo or Selena Gomez ever can.

Soon, machine learning will allow for this type of personal engagement at scale. It will also allow for small brands and businesses to authentically engage with customers without having to spend hours of their day on replies and comments.

As Jadali explains, The Gen Z audience is sensitive, intuitive and versatile, reaching them is not rocket science, it is not science at all, it is an art. It is something that anyone can master, wield and utilize.

Gen Z will help push Machine Learning to become more human, more perfectly imperfect in its responses, and move us closer to contextual AI in marketing and online content.

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Is Machine Learning The Key To Unlocking Gen Z Engagement? A Discussion With Jonathan Jadali Of Ascend - Forbes

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Global Machine Learning & Big Data Analytics Education Market Size will be Expanded and Reach Millions $ by 2024 KSU | The Sentinel Newspaper -...