Education Entrepreneur Sees AI and Human History at a Crossroads
Enver Yucel is the founder of BAU Global, a broad education network headquartered in Turkey, consisting of five universities, three language schools, four academic centers and one boarding school spread across North America, Europe, Africa and Asia. Yucel has devoted his life to education, having served an estimated 150,000 students since starting his first institution with three class rooms in Istanbul in 1974. He is also a member of the Advisory Board of the UN Institute for Training and Research. He was invited to speak at the AI World Conference & Expo in Boston in the fall of 2019, the first Turkish speaker in the four years of the conference. He recently took some time to answer questions posed by AI Trends Editor John P Desmond, who was in the audience for his Boston talk.
(Ed. Note: He delivered his address in Boston in Turkish, and a translator repeated it in English. This interview was conducted via email with Enver and his team answering questions we posed based on his talk in Boston, in Turkish, then translating them for us into English. We received the interview in early February 2020.)
AI Trends: Where does artificial intelligence fit in the historical context? (foundation of the universe, birth of life, beginning of artificial intelligence)
Yucel: Steam, electricity and computers … The inventions discovered in every industrial revolution have been used in all areas of life, in all sectors, all over the world. Transportation, production, agriculture, health and education…It is a stubborn fact that artificial intelligence has created a similar effect and will continue to do so. It is possible to see Artificial Intelligence applications in all areas of our life and in every sector. Artificial Intelligence supported systems; decide whether we can get a bank loan or not, and our waiting time when we order food to home; they draw a route on how to deliver it not only for the shortest but also for the easiest distance, conduct identification in the security field and even estimation who can pose a danger and enable unmanned vehicles to decide. That is to say, just as electricity transformed all sectors a century ago, now artificial intelligence does the same and enables the age to keep up with it.
According to some scientists it is the new electricity of the twenty-first century; according to others it is the birth of new hominid line. Whatever you call it, I can say that artificial intelligence and human history are at a crossroads and we will witness much more important changes in the coming years.
How can artificial intelligence be applied in the field of education?
The use of Artificial Intelligence in the field of education comes into question in many areas such as passing automation from administrative processes in schools to exam evaluations of teachers, preparation of the course contents clear-headedly, providing personalized education, providing education independent from time and place.
In addition to office automation in administrative processes in schools, thousands of applications can be automatically evaluated by artificial intelligence systems in matriculation transactions. Teachers can spend much more time with students by leaving their exams, homework evaluations, and feedback to the application of artificial intelligence. As we have been defending for decades, all education and learning models are specific to a person. A separate content and model can be determined for each student to provide student-based education. Student’s deficiencies are determined; accordingly, a lesson plan and content can be prepared appropriate for their deficiencies with much more detailed analysis except for orientation specific to course and subject.
What we have been defending in education from of old is while some students in the classroom are looking for total silence in the study environment, some provide concentration by listening to the latest music. From this point of view, thanks to virtual reality and the mixed reality environments that have emerged recently, those who wish can receive education in the way they want and wherever they want. Today, anyone can take online video lectures from the most prestigious educational institutions in the world. Why does not the student take this lesson interactively with the teacher’s hologram in his room? If technology makes this possible, why should education not keep up with it?
While today’s technology is developing accordingly, it is unacceptable that education continues with old methods. In order to catch up with the age, we must keep up with these changes, and even be pioneers. If a generation that no longer uses a computer and believes that it can handle everything with its mobile device is catching up, it is unacceptable to try to adapt this generation to the existing system; the system should embrace the needs of this generation.
What are the three most important issues facing humanity? (Ethics/values, inequality, poverty)
Poverty, inequality of income, environment and ethical values, culture of coexistence. We cannot live together. Secondly, we pollute the environment and the ethical world, and thirdly, we cannot live together. These 3 main fundamentals are the problems of the world.
What is Bau’s education mission?
Bahçeşehir University is a foundation university that aims to develop research and development for production, protection and dissemination of information that will raise the living standards of humanity; to raise critical, creative and entrepreneurial people who can carry out pioneering and innovative initiatives and studies which will increase the prosperity and happiness of Turkey and people of the world; to contribute to the problems faced by our people, government, industry and non-governmental organizations with creative solutions. BAU is a university that has adopted internationalization and acts with the vision of raising transnational global citizens.
What distinguishes BAU from other programs providing AI education?
Artificial Intelligence is one of the primary fields of our university. We attach importance to this issue both in terms of education and research. In our Faculty of Engineering and Natural Sciences, in addition to our many courses related to Artificial Intelligence at the undergraduate and graduate levels, “Artificial Intelligence for All” course is provided to all faculties in order to increase the awareness of artificial intelligence in our university. This course is open to all students, from our faculty of law to our faculty of communication. As of next year, we have plans to open an Artificial Intelligence program within our Computer and Software Engineering Departments and to accept students to these programs.
At the graduate level, we have applied for an Artificial Intelligence master program. In addition, we have been conducting Big Data, Cyber Security and Industry 4.0 graduate programs, perhaps the first in our country, in our Institute of Science for the last 4-5 years.
In terms of research, I can give an example of our Artificial Intelligence and Big Data Education and Research Centre, which we have established with the funds of Istanbul Development Agency, as well as national and internationally supported projects currently carried out by our faculty members. Through this centre, short-term training is given in line with the needs of the sector, and joint projects are carried out with different companies through our university-industry cooperation model.
Can you tell us which artificial intelligence technologies you use in your training presentation?
Big data analyses based applications are used in our institutions for years, especially in measurement and evaluation. However, I would like to tell you about our Metodbox Distance Education Platform, which we have launched in the past months and have introduced at the world’s largest training technologies fair last week.
The Metodbox Artificial Intelligence application is used in Bahcesehir Colleges and Ugur Schools in order to provide personalized education to our students. The Metodbox platform is a digital guide. The most important difference from its counterparts is that the student can follow what he can learn, and what the teacher can teach in this system, and they can develop themselves. The teacher can gather those, who know a subject the best, and can direct them to a higher level and students can continue their education on the subject with the difficulty they want. It is possible with the artificial intelligence-supported program that the teacher can make special procedures for the whole class or for every student in the class in the fields of exam preparation and reading the result, determining the subject of the lesson.
Which institutions or alternative programs for artificial intelligence education have you completed?
As a part of my profession and vision, I spend almost half of the year studying programs and institutions using the latest technology abroad. The seminars and briefings I received at the highest level in every institution I visit are training for me in a sense. In other words; I personally visit the most important centers of the world on site and receive training directly in the field.
How does your system support teachers?
Our primary goal is to increase the awareness of artificial intelligence of all our employees in our institutions and to ensure the use of the latest educational technologies in all of our training processes. In this sense, under the leadership of our university, all of our teachers in Bahçeşehir Colleges and Uğur Schools are provided with on-the-job training and their participation in external seminars is supported. For example, at the ‘Artificial Intelligence Conference in K12’ we will organize on February 10, panels, seminars and promotions will be held to teach our school principals about K12-level Artificial Intelligence with Artificial Intelligence.
Do you see potential dangers in the future of artificial intelligence? What kind of warnings do you have for taking measure?
The questions “Will the robots beat us? “Will they invade the world?” are continuously engaging people’s attention. At this point, perhaps the most important matter to be worried about is the “conscience” and “ethical” attribution to be made on artificial intelligence. The matters which are objective and will be accepted by everyone are absolutely available but how will subjective approaches be handled?
I think optimistically about this matter. There is no need to be afraid of Artificial Intelligence, it is in our hands to embrace it in an ethical framework and develop and use it in such a way that it will serve humanity. For this purpose, priority should be given to Human-Centred Artificial Intelligence studies, and Artificial Intelligence systems that learn from and collaborate with people should be developed.
I also think that research and discussions on Artificial Intelligence Ethics should be done in different platforms along with the academic world. Both the developed technologies should be ethical in terms of content and the studies on the R&D and ethical execution of the production process should be increased. However, I believe that politicians should be guided on the subject of legal regulations.
Learn more at BAU Global.
Is It Worth Investing in a Website Builder?
There are many different ways to build a website these days. There’s the timeless method of building your site code in Adobe Dreamweaver and exporting it to the web.
You can build a site in WordPress with a bit of CSS knowledge, or you can just outsource everything to a website design agency. Then there’s also the option of using a website builder, which is perhaps the easiest solution of all.
“Website builders are a popular way for people to easily and quickly set up a website with as little hassle as possible.”
They’re great for small retail businesses, whether you’re selling handmade crafts or drop shipping products from Amazon, but larger companies can effectively use website builders as well. They certainly aren’t for everyone, but let’s take a look at whether or not investing in a website builder is the right choice for you.
How much do website builders actually cost?
Website builders are always going to be cheaper than custom website design, and to an extent WordPress, but there are some variables. The thing is that some people (design agencies) like to point out that website builders cost a little more in things like domain hosting, SSL certificates, and other little monthly fees, compared to DIY hosting or a WordPress domain host.
So it becomes a question of upfront costs versus long-term costs in monthly fees, but there are several catches people don’t like to mention. Let me try to explain it succinctly.
Cost of a website builder
If you use a website builder to create, for example, a small eCommerce website. You’re probably going to pay around $200 ~ $500 upfront. This will include your domain name, any premium themes and add-ons (like a shopping cart module), and monthly hosting (which you’ll probably pay as an annual subscription upfront). It’s kind of like an “all-inclusive” vacation package, where everything is included in the total upfront cost.
So you’ll pay a small upfront fee which is mostly the annual hosting subscription, followed by monthly fees for the additional customizations you add to your website. Hosting plans on website builder platforms average around $9 to $75 per month, depending on your plan.
Again, it really depends on your plan, as website builders aren’t just for eCommerce websites. For example, there are a number of platforms which are built for specific industries, such as real estate, as this guide describes. Ultimately if you are going to use a website builder, it’s best to find one that is best suited to the industry you are operating in.
Cost of a WordPress website
If you use WordPress, you can expect to pay around $500 – $1,000 upfront for a similar small eCommerce website, with lower monthly fees. This is because you can shop around for a domain name and domain hosting from sites like HostGator, BlueHost, etc. to get the best subscription-based pricing available, but you’ll also be paying additionally for WordPress themes, mobile design plug-ins, shopping cart plug-ins, etc.
Using the vacation package analogy again, WordPress is like you’re paying for your own drinks, meals, and WiFi access at the resort.
This means that you’ll be spending a bit more upfront on piecing together the different elements of your website, but you’ll pay on average around $11 – $40 per month for domain hosting. Of course, you could also pay monthly for plug-in subscriptions, website maintenance, etc.
Cost of custom website design
So in the vacation package analogy, custom website design is like flying first-class to a resort, and you own the resort. Custom website design is going to cost a minimum of around $5,000 and could go much higher, depending on your web project.
“Website designers are paid around $50 – $100 per hour, and custom website design takes around 14 weeks on average, from beginning to launch.”
Now some website design agencies are going to be mad at me for saying this, but when they like to point out the “higher monthly costs” of a website builder, take a look at their fine print. Many website design agencies can lock you into monthly maintenance contracts, which can range from an additional $500 up to $3,000 per month or more, depending on the size of your site.
It’s kind of like if you have a contract with a car mechanic to inflate your tires and change your oil every month, except they keep billing you for a clutch assembly replacement. I’m not saying that website design agencies are dishonest, but you do need to be aware of what kind of monthly maintenance your website actually needs.
When we compare all three options (website builder, WordPress, and custom website design), it’s quite clear that website builders are the most affordable option. However, you’ll also be limited in customization options with a website builder, as you’re really piecing together templates and blocks, so you won’t get the exclusive customization and brand appeal you would with custom web design or a WordPress website. So you’ll have to consider what’s best for your long-term business plan.
Also, Read Tips to Automate Your Ecommerce
Amazon EC2 Inf1 instances featuring AWS Inferentia chips now available in five new Regions and with improved performance
Following strong customer demand, AWS has expanded the availability of Amazon EC2 Inf1 instances to five new Regions: US East (Ohio), Asia Pacific (Sydney, Tokyo), and Europe (Frankfurt, Ireland). Inf1 instances are powered by AWS Inferentia chips, which Amazon custom-designed to provide you with the lowest cost per inference in the cloud and lower barriers for everyday developers to use machine learning (ML) at scale.
As you scale your use of deep learning across new applications, you may be bound by the high cost of running trained ML models in production. In many cases, up to 90% of the infrastructure spent on developing and running an ML application is on inference, making the need for high-performance, cost-effective ML inference infrastructure critical. Inf1 instances are built from the ground up to support ML inference applications and deliver up to 30% higher throughput and up to 45% lower cost per inference than comparable GPU-based instances. This gives you the performance and cost structure you need to confidently deploy your deep learning models across a broad set of applications.
Customers and Amazon services adopting Inf1 instances
Since the launch of Inf1 instances, a broad spectrum of customers, such as large enterprises and startups, as well as Amazon services, have begun using them to run production workloads. Amazon’s Alexa team is in the process of migrating their Text-To-Speech workload from running on GPUs to Inf1 instances. INGA Technology, a startup focused on advanced text summarization, got started with Inf1 instances quickly and saw immediate gains.
“We quickly ramped up on AWS Inferentia-based Amazon EC2 Inf1 instances and integrated them in our development pipeline,” says Yaroslav Shakula, Chief Business Development Officer at INGA Technologies. “The impact was immediate and significant. The Inf1 instances provide high performance, which enables us to improve the efficiency and effectiveness of our inference model pipelines. Out of the box, we have experienced four times higher throughput, and 30% lower overall pipeline costs compared to our previous GPU-based pipeline.”
SkyWatch provides you with the tools you need to cost-effectively add Earth observation data into your applications. They use deep learning to process hundreds of trillions of pixels of Earth observation data captured from space every day.
“Adopting the new AWS Inferentia-based Inf1 instances using Amazon SageMaker for real-time cloud detection and image quality scoring was quick and easy,” says Adler Santos, Engineering Manager at SkyWatch. “It was all a matter of switching the instance type in our deployment configuration. By switching instance types to AWS Inferentia-based Inf1, we improved performance by 40% and decreased overall costs by 23%. This is a big win. It has enabled us to lower our overall operational costs while continuing to deliver high-quality satellite imagery to our customers, with minimal engineering overhead.”
AWS Neuron SDK performance and support for new ML models
You can deploy your ML models to Inf1 instances using the AWS Neuron SDK, which is integrated with popular ML frameworks such as TensorFlow, PyTorch, and MXNet. Because Neuron is integrated with ML frameworks, you can deploy your existing models to Amazon EC2 Inf1 instances with minimal code changes. This gives you the freedom to maintain hardware portability and take advantage of the latest technologies without being tied to vendor-specific software libraries.
Since its launch, the Neuron SDK has seen dramatic improvement in performance, delivering throughput up to two times higher for image classification models and up to 60% improvement for natural language processing models. The most recent launch of Neuron added support for OpenPose, a model for multi-person keypoint detection, providing 72% lower cost per inference than GPU instances.
The easiest and quickest way to get started with Inf1 instances is via Amazon SageMaker, a fully managed service for building, training, and deploying ML models. If you prefer to manage your own ML application development platforms, you can get started by either launching Inf1 instances with AWS Deep Learning AMIs, which include the Neuron SDK, or use Inf1 instances via Amazon Elastic Kubernetes Service (Amazon EKS) or Amazon Elastic Container Service (Amazon ECS) for containerized ML applications.
For more information, see Amazon EC2 Inf1 Instances.
About the Author
Michal Skiba is a Senior Product Manager at AWS and passionate about enabling developers to leverage innovative hardware. Over the past ten years he has managed various cloud computing infrastructure products at Silicon Valley companies, large and small.
Argonne National Labs Using AI To Predict Battery Cycles
By Allison Proffitt, Editorial Director, AI Trends
Thanks to the cost reductions that have come from global electric vehicle adoption, lithium ion batteries now have an important role to play in grid storage, Susan Babinec, Argonne National Laboratory, told audiences last week at the International Battery Virtual Seminar and Exhibit. But making full use of them is going to require a bit of help from artificial intelligence.
While EVs prize high energy density, and only need to last about eight years, grid applications require more cycles, more calendar life—20 to 30 years—and more safety at a lower cost.
“Grid economics requires precise life data, which is very time and resource intensive to generate,” Babinec said. “We are using approximations that create risk, limit our design creativity, and increase cost.” The solution? Of course, in today’s day and age the solution is always artificial intelligence, Babinec quipped. “In this case, we’re going to use AI to massively reduced time to cycle life prediction.”
Babinec’s team categorized the variables impacting lithium ion batteries for grid applications—acknowledging that adjusting any one variable will always mean changes in others. “For grid storage, first and foremost, low cost is always the most important,” Babinec said. But others include state-of-charge swing, C-rate, average state-of-charge, and temperature.
“Today we handle this variability by estimating the cycle life, but those estimates do not really allow us to push these cells to the limits of what they can really do,” Babinec said. “We just simply don’t have enough information on the cycle life and we are limited by the information that is provided by the cell manufacturer, which is really all about them making sure they can live up to their warranty.”
Babinec is prioritizing overall cost per cycle (levelized cost of storage, LCOS). This is a better metric than capital cost because grid storage batteries are durable goods, she explained. The Department of Energy’s target for LCOS is $0.02/kWh, a target for which we currently fall far short.
“No matter how you look at it, we are not there today with any combination of capital and cycles,” Babinec said. “We need to bring the capital down, but right here and now we need to bring the number of cycles up.”
Looking to AI to Decrease Testing Time from Two Years to Two Weeks
Argonne is applying artificial intelligence to the problem. Babinec’s group is developing rapid cycle life evaluations using AI to decrease testing from the current two years to a goal of two weeks. Argonne is the right spot for this research, Babinec argues. As the DOE’s battery hub, Argonne has plenty of data, a team of AI experts, and a new supercomputer up to the task. Aurora, created in partnership with Argonne, Cray and the DOE, will be the first exascale computer in the U.S.
The scope of the project is broad. They are using several AI approaches—from physics-based tools to deep neural nets. “We want to see which AI approach is the best for this problem,” Babinec said. All of the Li-ion chemistries will be tested deliberately and sequentially, and the current, voltage, and time will be recorded for every second, of every cycle, for every cell.
Babinec describes the basic AI process as encoding data from one cell running one cycle. Each cell cycle generates 150 features. Narrowing in on one feature from many cells, you determine correlations and relationships and decode for one behavior: cycles to failure.
To test their plan, the group used public data published last year in Nature Energy (DOI: 10.1038/s41560-019-0356-8). They compared the capacity at a certain voltage in cycle one to the capacity at the same voltage in cycle 20 and generated correlations and relationships then predictions from there. The results: the experimental cycles to failure and the predicted cycles to failure aligned.
Her presentation at Florida Battery was the first presentation of Argonne’s experimental results, and Babinec shared that the approach seems to be working. When testing many chemistries, like cells self-organize by chemistry and cycles to failure. When run on real cells, predictions match observed. So far, Babinec says it looks like it will take as few as 40-60 cycles to predict cycle life—more for high cycle life, less for low cycle life.
The key to a high-quality prediction, she emphasized, is using training data from cells with a cycle life that is similar to your goal cycle life. For example, cells that failed at 150 cycles will not accurately train an algorithm to predict 2,000 cycles.
While work on the cycle life predictions continues, Babinec says Argonne is also focused on cleaning up more than 20 years’ worth of spreadsheets, databases, and machine files containing battery data. “The data is wonderful, but it has to be cleaned up. It’s a major effort, which we are working on,” she said. The team is working toward machine learning-ready training data including, for example, capacity vs. cycle comparisons and discharge curves. Some data are available on Github: https://github.com/materials-data-facility/battery-data-toolkit
“There is promise for this,” Babinec said. Testing timelines will decrease, which she says may open up assessments of complex and changing use scenarios, eventually enhancing deployment flexibility while minimizing risk.
Learn more at Nature Energy (DOI: 10.1038/s41560-019-0356-8).
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