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With the IU and LSBU (London South Bank University) dual degree track, you get a unique opportunity you can choose if you want to graduate with both a German and a British graduation certificate, without any extra academic requirements. The study programmes at IU and at LSBU are coordinated and therefore equivalent to each other.

Start your studies at IU, and if you want to apply for your British certificate* all you have to do is send in your application and pay the required fee. Youll then be awarded a degree from LSBU following your graduation if all of your study requirements have been fulfilled successfully.

Graduate with a German Bachelors, MBA or Masters degree along with a UK Bachelors with Honours (Hons), MBA or Masters.

London South Bank University is well-known for its impressive internationality, as testified by over 18,000 students from more than 130 countries. Similar to IU, LSBU has also been awarded multiple awards and praised for its focus on improving graduates career opportunities.

Our cooperation was born out of one goal: to help you get the best jobs in the world with a dual degree.

Get in touch with our Student Advisory Team, send in your application form and receive your British graduation certificate after youve successfully graduated from IU.

*only available for selected study programmes: B.Sc. Data Science, B.Sc. Computer Science, B.A.A. Business Administration, B.A. International Management, M.Sc. Artificial Intelligence, M.Sc. Computer Science, M.Sc. Data Science, M.A. Master Management with electives (Engineering, Finance & Accounting, Int. Marketing, IT, Leadership, Big Data), MBA with electives (Big Data, Engineering, Finance & Accounting, IT, Marketing).

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Master in Artificial Intelligence Online | IU International

The Artificial Intelligencemaster's degree program is designed as a 30-credit hour curriculum that give students a comprehensive framework for artificial intelligence with one of 4 concentration areas: (1) Computer Vision, (2) Intelligent Interaction, (3) Machine Learning, and (4) Knowledge Management and Reasoning.

Students will engage in an extensive core intended curriculum to develop depth in all the core concepts that build a foundation for artificial intelligence theory and practice. Also, they will be given the opportunity to build on the core knowledge of AI by taking a variety of elective courses selected from colleges throughout campus to explore key contextual areas or more complex technical AI applications.

The program will be accessible to both full-time and part-time students, aiming to train students who aspire to have AI research and development (R&D) or leadership careers in industry. To accommodate the needs of working professionals who might be interested in this degree program, the course offerings for the MS in AI will be in the late afternoon and evening hours to allow students to earn the degree through part-time study. The program may be completed entirely on campus, entirely online, or through a combination of on-campus and online courses.

If you have additional questions, please contact the program director: Dr. Jin Lu (jinluz@umich.edu).

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MS in Artificial Intelligence | University of Michigan-Dearborn

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Register today!

More than ever, organizations are putting their confidence and investment into the potential of artificial intelligence (AI) and machine learning (ML).

According to the 2022 IBM Global AI Adoption Index, 35% of companies report using AI today in their business, while an additional 42% say they are exploring AI. Meanwhile, a McKinsey survey found that 56% of respondents reported they had adopted AI in at least one function in 2021, up from 50% in 2020.

But can investments in AI deliver true ROI that directly impacts a companys bottom line?

According to Domino Data Labs recent REVelate survey, which surveyed attendees at New York Citys Rev3 conference in May, many respondents seem to think so. Nearly half, in fact, expect double-digit growth as a result of data science. And 4 in 5 respondents (79%) said that data science, ML and AI are critical to the overall future growth of their company, with 36% calling it the single most critical factor.

Implementing AI, of course, is no easy task. Other survey data shows another side of the confidence coin. For example, recent survey data by AI engineering firm CognitiveScale finds that, although execs know that data quality and deployment are critical success factors for successful app development to drive digital transformation, more than 76% arent sure how to get there in their target 12-18 month window. In addition, 32% of execs say that it has taken longer than expected to get an AI system into production.

ROI from AI is possible, but it must be accurately described and personified according to a business goal, Bob Picciano, CEO of Cognitive Scale, told VentureBeat.

If the business goal is to get more long-range prediction and increased prediction accuracy with historical data, thats where AI can come into play, he said. But AI has to be accountable to drive business effectiveness its not sufficient to say a ML model was 98% accurate.

Instead, the ROI could be, for example, that in order to improve call center effectiveness, AI-driven capabilities ensure that the average call handling time is reduced.

That kind of ROI is what they talk about in the C-suite, he explained. They dont talk about whether the model is accurate or robust or drifting.

Shay Sabhikhi, co-founder, and COO at Cognitive Scale, added that hes not surprised by the fact that 76% of respondents reported having trouble scaling their AI efforts. Thats exactly what were hearing from our enterprise clients, he said. One problem is friction between data science teams and the rest of the organization, he explained, that doesnt know what to do with the models that they develop.

Those models may have potentially the best algorithms and precision recall, but sit on the shelf because they literally get thrown over to the development team that then has to scramble, trying to assemble the application together, he said.

At this point, however, organizations have to be accountable for their investments in AI because AI is no longer a series of science experiments, Picciano pointed out. We call it going from the lab to life, he said. I was at a chief data analytics officer conference and they all said, how do I scale? How do I industrialize AI?

However, not everyone agrees that ROI is even the best way to measure whether AI drives value in the organization. According to Nicola Morini Bianzino, global chief technology officer, EY, thinking of artificial intelligence and the enterprise in terms of use cases that are then measured through ROI is the wrong way to go about AI.

To me, AI is a set of techniques that will be deployed pretty much everywhere across the enterprise there is not going to be an isolation of a use case with the associated ROI analysis, he said.

Instead, he explained, organizations simply have to use AI everywhere. Its almost like the cloud, where two or three years ago I had a lot of conversations with clients who asked, What is the ROI? Whats the business case for me to move to the cloud? Now, post-pandemic, that conversation doesnt happen anymore. Everybody just says, Ive got to do it.

Also, Bianzino pointed out, discussing AI and ROI depends on what you mean by using AI.

Lets say you are trying to apply some self-driving capabilities that is, computer vision as a branch of AI, he said. Is that a business case? No, because you cannot implement self-drivingwithout AI. The same is true for a company like EY, which ingests massive amounts of data and provides advice to clients which cant be done without AI. Its something that you cannot isolate away from the process its built into it, he said.

In addition, AI, by definition, is not productive or efficient on day one. It takes time to get the data, train the models, evolve the models and scale up the models. Its not like one day you can say, Im done with the AI and 100% of the value is right there no, this is an ongoing capability that gets better in time, he said. There is not really an end in terms of value that can be generated.

In a way, Bianzino said, AI is becoming part of the cost of doing business. If you are in a business that involves data analysis, you cannot not have AI capabilities, he explained. Can you isolate the business case of these models? It is very difficult and I dont think its necessary. To me, its almost like its a cost of the infrastructure to run your business.

Kjell Carlsson, head of data science strategy and evangelism at enterprise MLops provider Domino Data Lab says that at the end of the day, what organizations want is a measure of the business impact of ROI how much it contributed to the bottom line. But one problem is that this can be quite disconnected from how much work has gone into developing the model.

So if you create a model which improves click-through conversion by a percentage point, youve just added several million dollars to the bottom line of the organization, he said. But you could also have created a good predictive maintenance model which helped give advance warning to a piece of machinery needing maintenance before it happens. In that case, the dollar-value impact to the organization could be entirely different, even though one of them might end up being a much harder problem, he added.

Overall, organizations do need a balanced scorecard where they are tracking AI production. Because if youre not getting anything into production, then thats probably a sign that youve got an issue, he said. On the other hand, if you are getting too much into production, that can also be a sign that theres an issue.

For example, the more models data science teams deploy, the more models theyre on the hook for managing and maintaining, he explained. So you deployed this many models in the last year, so you cant actually undertake these other high-value ones that are coming your way, he explained.

But another issue in measuring the ROI of AI is that for a lot of data science projects, the outcome isnt a model that goes into production. If you want to do a quantitative win-loss analysis of deals in the last year, you might want to do a rigorous statistical investigation of that, he said. But theres no model that would go into production, youre using the AI for the insights you get along the way.

Still, organizations cant measure the role of AI if data science activities arent tracked. One of the problems right now is that so few data science activities are really being collected and analyzed, said Carlsson. If you ask folks, they say they dont really know how the model is performing, or how many projects they have, or how many CodeCommits your data scientists have made within the last week.

One reason for that is the very disconnected tools data scientists are required to use. This is one of the reasons why Git has become all the more popular as a repository, a single source of truth for your data scientist in an organization, he explained. MLops tools such as Domino Data Labs offer platforms that support these different tools. The degree to which organizations can create these more centralized platformsis important, he said.

Wallaroo CEO and founder Vid Jain spent close to a decade in the high-frequency trading business in Merrill Lynch, where his role, he said, was to deploy machine learning at scale and and do so with a positive ROI.

The challenge was not actually developing the data science, cleansing the data or building the trade repositories, now called data lakes. By far, the biggest challenge was taking those models, operationalizing them and delivering the business value, he said.

Delivering the ROI turns out to be very hard 90% of these AI initiatives dont generate their ROI, or they dont generate enough ROI to be worth the investment, he said. But this is top of mind for everybody. And the answer is not one thing.

A fundamental issue is that many assume that operationalizing machine learning is not much different than operationalizing a standard kind of application, he explained, adding that there is a big difference, because AI is not static.

Its almost like tending a farm, because the data is living, the data changes and youre not done, he said. Its not like you build a recommendation algorithm and then peoples behavior of how they buy is frozen in time. People change how they buy. All of a sudden, your competitor has a promotion. They stop buying from you. They go to the competitor. You have to constantly tend to it.

Ultimately, every organization needs to decide how they will align their culture to the end goal around implementing AI. Then you really have to empower the people to drive this transformation, and then make the people that are critical to your existing lines of business feel like theyre going to get some value out of the AI, he said.

Most companies are still early in that journey, he added. I dont think most companies are there yet, but Ive certainly seen over the last six to nine months that theres been a shift towards getting serious about the business outcome and the business value.

But the question of how to measure the ROI of AI remains elusive for many organizations. For some there are some basic things, like they cant even get their models into production, or they can but theyre flying blind, or they are successful but now they want to scale, Jain said. But as far as the ROI, there is often no P&L associated with machine learning.

Often, AI initiatives are part of a Center of Excellence and the ROI is grabbed by the business units, he explained, while in other cases its simply difficult to measure.

The problem is, is the AI part of the business? Or is it a utility? If youre a digital native, AI might be part of the fuel the business runs on, he said. But in a large organization that has legacy businesses or is pivoting, how to measure ROI is a fundamental question they have to wrestle with.

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The truth about AI and ROI: Can artificial intelligence really deliver? - VentureBeat

For years, physicists have been making major advances and breakthroughs in the field using their minds as their primary tools. But what if artificial intelligence could help with these discoveries?

Last month, researchers at Duke University demonstrated that incorporating known physics into machine learning algorithms could result in new levels of discoveries into material properties, according to a press release by the institution. They undertook a first-of-its-kind project where theyconstructed a machine-learning algorithm to deduce the properties of a class of engineered materials known as metamaterials and to determine how they interact with electromagnetic fields.

The results proved extraordinary. The new algorithm accurately predicted the metamaterials properties more efficiently than previous methods while also providing new insights.

By incorporating known physics directly into the machine learning, the algorithm can find solutions with less training data and in less time, said Willie Padilla, professor of electrical and computer engineering at Duke. While this study was mainly a demonstration showing that the approach could recreate known solutions, it also revealed some insights into the inner workings of non-metallic metamaterials that nobody knew before.

In their new work, the researchers focused on making discoveries that were accurate and made sense.

Neural networks try to find patterns in the data, but sometimes the patterns they find dont obey the laws of physics, making the model it creates unreliable, said Jordan Malof, assistant research professor of electrical and computer engineering at Duke. By forcing the neural network to obey the laws of physics, we prevented it from finding relationships that may fit the data but arent actually true.

They did that by imposing upon the neural network a physics called a Lorentz model. This is a set of equations that describe how the intrinsic properties of a material resonate with an electromagnetic field. This, however, was no easy feat to achieve.

When you make a neural network more interpretable, which is in some sense what weve done here, it can be more challenging to fine tune, said Omar Khatib, a postdoctoral researcher working in Padillas laboratory. We definitely had a difficult time optimizing the training to learn the patterns.

The researchers were pleasantly surprised to find that this model workedmore efficiently than previous neural networks the group had created for the same tasks by dramatically reducing the number of parameters needed for the model to determine the metamaterial properties. The new model could evenmake discoveries all on its own.

Now, the researchers are getting ready to use their approach on unchartered territory.

Now that weve demonstrated that this can be done, we want to apply this approach to systems where the physics is unknown, Padilla said.

Lots of people are using neural networks to predict material properties, but getting enough training data from simulations is a giant pain, Malof added. This work also shows a path toward creating models that dont need as much data, which is useful across the board.

The study is published in the journal Advanced Optical Materials.

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Artificial intelligence has reached a threshold. And physics can help it break new ground - Interesting Engineering

Have you ever wanted to see a polar bear riding a skateboard? What about a hot dog wearing a tracksuit?

Well, if youll settle for AI-generated images of those things or anything else you can dream up then youll appreciate DALLE mini, the free website currently taking over the internet.

It may sound like sci-fi, but the premise is simple: On your phone or computer, go to huggingface.co/spaces/dalle-mini/dalle-mini. Type out any prompt in the text box for example, Dak Prescott holding a banana. Hit the button that says Run (you may need to hit it multiple times before traffic subsides and your request goes through).

Eventually, nine images generated completely by artificial intelligence will appear, bringing your concept to life with varying levels of accuracy and hilarity. In the case of Dak Prescott holding a banana, the results were good for a laugh, but stopped short of realism see below.

The ripe-for-memes program was created by Boris Dayma, a machine learning engineer based in Houston. He made the website available for public use last year, but only in the past two weeks has it taken off in social media popularity, with users sharing images of everything from Darth Vader ice fishing to Karl Marx making an appearance in Seinfeld. A Twitter account that shares some of the weirdest creations has racked up over 600,000 followers.

Dayma was inspired to build the program after reading a research paper about DALLE, a sophisticated text-to-image artificial intelligence program created by OpenAI, an artificial intelligence company co-founded by Elon Musk. Last summer, as part of a program organized by the AI company Hugging Face, Dayma and a team developed DALLE mini, a scaled-down version that, unlike the original program, is open to the public. (There is currently a waitlist to access the original DALLE).

Being able to create an image that looks like what you wanted, on the technical level, to me, it was very interesting, said Dayma. I want to be able to try it out myself and I want to be able to let other people use it.

The way the DALLE mini program works, Dayma said, is by processing images and captions from across the internet. Slowly, the program begins to discern patterns, such as a visual patch of blue when the caption indicates sky. When a user types in a text prompt, the program, using these associations, will try to put it together to make something that makes sense, Dayma said.

It learns very tiny concepts like that, and over time, it becomes better and better, he said.

Demand for the app, Dayma confirmed, has soared as of late. Many users now complain of getting a pop-up saying, Too much traffic, please try again, when they try to generate images.

We obviously didnt plan for such crazy traffic, so weve been working on improving the code, improving the model, said Dayma. People seem to like it, so they need to be able to use it.

Despite the wait times, Dayma said the public nature of the program is an asset to the technology. Beyond the futuristic entertainment it provides the masses, the program is open source, meaning the code is publicly available, so some people are able to play with the model itself and program and tweak it, he said. Since he is still training the model to produce better images, input from other users proves valuable.

People can learn about the limitations of the model, the biases, what its good at, what it can be used for, he said. Everybody can benefit from having a public model like this.

After improvements are made on the traffic capacity and the model itself, Dayma said, the sky is the limit. You can generate videos, you can generate music, he said. Its a new area thats opening up.

Its an area, however, thats fraught with controversy. Experts have raised concerns that artificial intelligence technology will perpetuate biases or promote disinformation. But with DALLE mini, Dayma said, the quality is just not there for most people to be fooled by the images at least for now. By bringing AI out of the ivory towers of Silicon Valley and into the hands of anybody with a smartphone, Dayma said, he is hoping not only to amuse, but also to sound the alarm.

At least people can learn that that type of thing is coming, and now you need to be aware with the content that you see online, he said. I hope it helps people develop their critical thinking.

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DALL-E mini is the viral artificial intelligence artist taking over Twitter - The Dallas Morning News