In a series of videos UF posted on April Fools’ Day, UF President Kent Fuchs fired himself and the rest of UF’s administrative staff, leaving decisions up to the software of UF’s artificially intelligent supercomputer.

While UF administration won’t abandon Tigert Hall any time soon, AI capabilities on campus really are advancing.

Fuchs is known for his yearly April Fools’ jokes, but didn’t pull one last year because of the onset of COVID-19. In 2019, Fuchs debuted his new football uniform as a prank. The return of the joke this year played on UF’s growing AI program and the fears some people have about AI technology. UF’s AI program, which has $70 million in funding, is unique because while most universities with AI technology usually only use it for programs related to computer science, UF plans to use AI within every college at the university.

As UF’s AI system goes through its early rollout stages, fears of job automation may become more realized around campus. But UF administrators are urging people not to panic.

“The role of AI is not to replace people. It’s to make people more effective,” UF president Kent Fuchs said. He said he likes using the phrase “amplified intelligence” to make that clear.

UF hopes to hire 100 new staff members who specialize in AI technology by Fall, Fuchs said. Additionally, the university looks to hire specialists in AI ethics to ensure the technology is deployed responsibly. This is important because experts in AI are concerned about how bias seeps into these machines, according to the Harvard Business Review. A ProPublica investigation found an AI algorithm in the Broward County justice system mislabeled Black defendants as high-risk twice as much as it did white defendants. 

UF’s AI system was added to the school’s supercomputer, HiPerGator, last Summer. Hardware tech company Nvidia donated the system along with $25 million to help implement the program across campus. The company’s co-founder Chris Malachowsky, a UF alumni, matched Nvidia’s donation. Meanwhile, UF is pouring $20 million of its own money into the project. These funds are going toward building Malachowsky Hall, which will house several programs that focus on AI use.

UF aims to offer its newest technology to students pursuing any degree, whether it’s related to arts or agriculture, Fuchs said. Once the technology is fully in place, he wants everyone at UF to have some knowledge of AI by the time they graduate.

But UF students and faculty won’t be the only ones benefiting from the AI technology. Fuchs called all the presidents of universities within the Southeastern Conference and the State University System of Florida to offer them a chance to participate in AI research along with UF. He wants UF to help other institutions implement AI technology and serve as an example for universities across the nation. 

For now, UF is focused on incorporating AI into its own programs. 

Gators coach Dan Mullen doesn’t have to worry about losing his job to a robot anytime soon, but he can look forward to getting help from one in the very near future. 

UF’s Institute of Coaching will be one of the many programs to have access to AI technology. The technology will help coaches understand data and make decisions on recruiting, hiring and player assignments, Michael Reid, dean of UF’s College of Health and Human Performance, said.

Another possibility of AI is monitoring player performance, which would be done by placing wearable technology onto players, Reid said. The devices would record data such as heart rate and acceleration rate that can later be used to help athletes improve their abilities on the field.

Advancing athletic performance with data analytics is already widely used among sports teams and companies, Reid said, for things such as measuring a baseball pitcher’s pitch velocity. UF’s technology would aim to improve the way data is interpreted and used by coaches.

UF’s College of Medicine will also use the AI technology to enhance data analytics, Patrick Tighe, an associate professor at UF’s Department of Anesthesiology, said. The data would better a doctor’s ability to predict the benefits a patient may get from surgery, how long it will take a patient to recover from surgery and whether a patient is at risk for illness after surgery. 

It’s also important to research ethics in using AI for medical data, Tighe said. Researchers at UF’s Health Science Center are already thinking about how AI tools can be misused, Tighe said. Researchers at UF want to make sure the data is used fairly and accurately.

The algorithms have to be able to represent groups equally to ensure data is fair and accurate, Tighe said. UF Researchers are making sure to emphasize the importance of collecting data from medically diverse groups of patients so doctors can individualize treatment.

“One of the core themes for this academic hub and artificial intelligence revolves around the concept of trustworthy AI,” Tighe said. 

AI will not replace certain human medical functions like physical therapy, nursing care and pharmacy care anytime soon, Tighe said.

“Taking care of other human beings who are ill, there’s nothing artificial about that. That requires a human touch,” Tighe said.

Although some professors may drone on during lectures at times, actual drones won’t replace the last human aspect of online classes this April Fools’ Day.

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Florida State University’s College of Arts and Sciences has announced the launch of a new interdisciplinary graduate program that will welcome its first students in Fall 2021. 

The FSU Interdisciplinary Data Science Master’s Degree Program, or IDS, leverages FSU’s strengths in computer science, mathematics, scientific computing and statistics to prepare students for contemporary careers in data science, one of the fastest growing fields in the United States.  

“The FSU IDS program is innovative and timely, and it will help address the nation’s demand for data science practitioners,” said Sam Huckaba, the college’s dean. “Those involved with building the curriculum have done an outstanding job identifying essential material that will help provide graduates with analytical tools and necessary skills.” 

The new program will be delivered exclusively at FSU’s Tallahassee campus. Students will complete a series of core courses providing a solid starting point in mathematics, machine learning, statistics, data ethics and databases, along with electives that support a specific major area of study — computer science, mathematics, scientific computing or statistics. Nineteen faculty members from across the program’s major disciplines will guide students through coursework and major selections that meet their individual needs and goals. 

“The interdisciplinary data science program is a wonderful addition to our slate of academic offerings,” said Provost Sally McRorie. “Many fields are now requiring a workforce that not only possesses a grasp of big data but also knows how to analyze and generate meaning from that data. At Florida State, we take pride in making sure our students are career ready, and the IDS program will create even more opportunities for our students.” 

Graduates of the FSU Interdisciplinary Data Science Master’s Degree Program will fill a growing demand for a workforce trained in data science and possess sought-after skills to read, analyze, explore, model and draw conclusions from the highly complex, multi-dimensional, rapidly expanding and diverse data universe.  

“The data science market is projected to be worth $103 billion by 2023, and the U.S. Bureau of Labor Statistics projects jobs for computer and information research scientists, and data scientists will experience 14 percent growth through 2028,” said Gordon Erlebacher, interim IDS program director and professor of scientific computing. “Learning how to responsibly collect, analyze, and apply data to a variety of fields will be key to success for FSU students now and into the future.” 

A number of alumni from IDS’ affiliated programs have landed jobs in the data science industry, but the ability to earn a degree in data science at FSU will cement future graduates’ preparation and marketability to employers. Sectors where data science skills will prove indispensable include cybersecurity, data information processing, financial services, epidemiology, public health, survey research, airline and auto industries, real estate, online retail, and more. 

FSU’s IDS program has been designed to appeal to students from a wide range of undergraduate backgrounds. 

“The program will appeal to any student holding a degree in a mathematical sciences discipline, but it also will be of interest to students from other disciplines, such as the physical sciences and engineering, or in fact any discipline, who have satisfied baseline prerequisites,” Huckaba said. “Further, the IDS platform is designed to accommodate major tracks all across campus.” 

While there are other data science education programs in Florida’s State University System and the U.S., the FSU IDS program is unique in its inclusion of ethics and communication as part of the core curriculum.   

“Spending time educating our students on the ethics related to data mining and data extraction and communication skills and strategies will strengthen their sense of responsibility and increase the value they bring to potential employers after graduation,” Erlebacher said. 

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Defence Institute of Advanced Technology (DIAT), a deemed university and an autonomous organisation funded by the Defence PSU to offer two online courses– short term programme on Artificial Intelligence & Machine Learning and online training and certification course on cybersecurity

Defence Research and Development Organization (DRDO) has announced two online courses– short term programme on Artificial Intelligence & Machine Learning and online training and certification course on cybersecurity. These two courses would be organised by the Defence Institute of Advanced Technology (DIAT), a deemed university and an autonomous organisation funded by the Defence PSU. 

Both of these programmes are 12-weeks long, which would consist of two-hour sessions for five days a week. 

For the course on artificial intelligence and machine learning, selected candidates will be trained on probability theory, pattern recognition, big data analytics, computer vision, natural language processing, augmented reality, deep learning, and other fundamental and advanced AI/ML topics.

Interested candidates will have to take an entrance test which will be conducted on February 20. The eligibility criteria for those appearing in the exam is that they should hold a graduate degree in any stream. They will be tested on their programming knowledge, basics of algorithms, databases, data structure, statistics, probability, and modular mathematics. 

The other course on Cybersecurity would deal with the fundamentals and advanced topics such as forensic and incident response, system programming, reverse engineering and malware analysis, basic and advanced vulnerability analysis, exploit mitigation, and penetration testing. Students will also be trained on the tools and techniques required for Cyber Security professionals through the 12-week course.

For this course as well, students will have to take an entrance exam which will be held on February 21. They will be asked questions on the fundamentals of operating systems, networking, system software, data structures and programming languages.

The registration window for both the courses is open between January 28 and February 15 and the aspiring candidates may register themselves on onlinecourse.diat.ac.in, for free. 

The results will be announced on February 22. Once selected, the students will have to pay a fee of Rs 15,000 with February 26 after which the courses will commence from February 28. 

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SINGAPORE, July 15, 2019 /PRNewswire/ — Today at RSA Asia Pacific & Japan 2019, API security leader and creator of the industry’s first API Firewall – 42Crunch – announced the latest release of its API security platform with full support for Kubernetes environments. This new solution allows organizations to easily automate API security across Kubernetes environments – enabling the zero-trust architecture needed to protect each microservice, and scale without risk. 

The rapid adoption of microservices architectures and Kubernetes lead to proliferation of APIs exposed by these microservices. Developers employ agile practices to quickly iterate on these microservices. Combined, these trends lead to hundreds if not thousands of rapidly changing APIs that modern enterprises often host and need to secure.

Traditional solutions such as Web Application Firewalls (WAF) and API Management tools rely on static rules and policies, and edge protection. While these solutions provide some security functionality within your environment, they still leave the individual microservices vulnerable to API attacks. 

Through a fully automated platform, 42Crunch extends security beyond the edge of the enterprise to each individual microservice, protecting them with an ultra-low latency micro API firewall that can be deployed at scale. 42Crunch API firewall is merely 20 MB in size and when deployed in sidecar proxy mode in Kubernetes pods enforces API security with sub-millisecond overhead. This eliminates the manual process of writing and maintaining individual API security policies, and enforces a zero-trust security architecture.

“Since the initial launch of the 42Crunch API Security platform our customers have informed us that edge protection is no longer enough,” says Jacques Declas, CEO and founder of 42Crunch. “We are excited to make our Kubernetes-native API protection commercially available. Now the teams working on large numbers of microservices can be sure that each and every one of them automatically stays secure throughout its lifecycle.”

In addition, 42Crunch’s unique approach integrates with companies’ DevSecOps pipeline and delivers automated API security across the whole API lifecycle:

• AUDIT: Run 200+ security audit tests of the OpenAPI specification definition with detailed security scoring to help developers define and strengthen API contract.
• SCAN: Scan live API endpoints to discover potential vulnerabilities and discrepancies of the API implementation against the API contract.
• PROTECT: Launch service to protect APIs and apply policies that can be deployed in our lightweight, low-latency micro API firewall.

42Crunch will be participating in RSA Asia Pacific & Japan 2019 as both an exhibitor and speaker. Join Matthieu Estrade, CTO, at 9:00 on Thursday July 18th for his talk: “API Security: Learning from the 20 Years of AppSec Failures,” located in Orchid 4203. Visit the 42Crunch team at booth 1708 to learn more about how we can help you automate API security in your microservices environment.

About 42Crunch
42Crunch bridges the gap between API development and security teams with a simple, automated platform that provides auditing, live endpoint scanning, and micro API firewall protection. Unlike other solutions on the market, the 42Crunch platform empowers development, security and operations teams with a set of integrated tools to easily build security into the foundation of the API and enforce those policies throughout the API lifecycle. By delivering security as code you enable a seamless DevSecOps experience, allowing innovation at the speed of business without sacrificing integrity. Visit https://42crunch.com to learn more. 

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Yesterday, the team at PyTorch announced the availability of PyTorch Hub which is a simple API and workflow that offers the basic building blocks to improve machine learningresearch reproducibility.

Reproducibility plays an important role in research as it is an essential requirement for a lot of fields related to research including the ones based on machine learning techniques. But most of the machine learning based research publications are either not reproducible or are too difficult to reproduce.

With the increase in the number of research publications, tens of thousands of papers being hosted on arXiv and submissions to conferences, research reproducibility has now become even more important. Though most of the publications are accompanied by code and trained models that are useful but still it is difficult for users to figure out for most of the steps, themselves.

PyTorch Hub consists of a pre-trained model repository that is designed to facilitate research reproducibility and also to enable new research. It provides built-in support for Colab, integration with Papers With Code and also contains a set of models including classification and segmentation, transformers, generative, etc. By adding a simple hubconf.py file, it supports the publication of pre-trained models to a GitHub repository, which provides a list of models that are to be supported and a list of dependencies that are required for running the models.

For example, one can check out the torchvision, huggingface-bert and gan-model-zoorepositories. Considering the case of torchvision hubconf.py: In torchvision repository, each of the model files can function and can be executed independently. These model files don’t require any package except for PyTorch and they don’t need separate entry-points.

A hubconf.py can help users to send a pull request based on the template mentioned on the GitHub page.

The official blog post reads, “Our goal is to curate high-quality, easily-reproducible, maximally-beneficial models for research reproducibility. Hence, we may work with you to refine your pull request and in some cases reject some low-quality models to be published. Once we accept your pull request, your model will soon appear on Pytorch hub webpage for all users to explore.”

PyTorch Hub allows users to explore available models, load a model as well as understand the kind of methods available for any given model. Below mentioned are few of the examples:

Explore available entrypoints:

With the help of torch.hub.list() API, users can now list all available entrypoints in a repo.  PyTorch Hub also allows auxillary entrypoints apart from pretrained models such as bertTokenizer for preprocessing in the BERT models and making the user workflow smoother.

Load a model:

With the help of torch.hub.load() API, users can load a model entrypoint. This API can also provide useful information about instantiating the model.

Most of the users are happy about this news as they think it will be useful for them. A user commented on HackerNews, “I love that the tooling for ML experimentation is becoming more mature. Keeping track of hyperparameters, training/validation/test experiment test set manifests, code state, etc is both extremely crucial and extremely necessary.”

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