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[WATCH] Replay of our Machine Learning and Finance Meetup
[WATCH] Replay of our Industrial Application Data Science Meetup
Darwin Recruitment returned to Copenhagen for its second Data Science MeetUp. This time, we had three speakers talking about the industrial application of Data Science. Our Data Science Specialist Luke Parkinson and our Team Leader for Denmark were in attendance with more than 50 Data professionals in the audience. Speakers and Topics Dmytro Iakubovskyi - Postdoctoral Data Scientist and Machine Learning Engineer – Discovery Center, Copenhagen University Academic researchers are thought to be well apart from the industry. But there is also a constant demand for academic researchers in rapidly evolving industries, such as Data Science. For such a transition, the majority of recommendations is concentrated on new skills that need to be gained by an academic researcher. In this talk, I will focus on details on specific capabilities of academic researchers that are in high demand in industry-based Data Science. Jakob Rasmussen – CEO - Apex AI The video game industry has been late in adopting machine learning. This is now changing, with many new initiatives such as EA Games' SEED lab and Unity incorporating AI into the core game engine. AI might thus transform the video game industry in ways it is transforming other industries. However, AI for the video game industry can not necessarily use the same approaches as AI for other industries, as some of the challenges to good AI in video games are super complex. To address these challenges, I will in this talk present some of the approaches Apex AI has used for machine learning in the upcoming title Unleash, and what we have learned from that process about developing AI for video games. Neil Hodgson – Head of Asset Science – Maersk Digital Every year Maersk transports over 1 million refrigerated containers carrying everything from fruit to pharmaceuticals in controlled temperature and atmospheric conditions. Each container reports back its status every hour, giving us a large amount of data about the journey. In this talk, I'll show you how data science is used every step of the way as your bananas make their way from South America to the shelves of your local supermarket in Denmark.
When Will Artificial Intelligence (AI) Replace Web Development & Frontend Developers?
Artificial intelligence (AI) has made a huge impact in web development, frontend developer jobs and artificial intelligence jobs. This is not a case of artificial intelligence vs web development; they have to work together. Artificial intelligence in web development/web design is becoming more and more common and the impact of artificial intelligence on web development is clear. We can see the rise in artificial intelligence popularity grow rapidly here: We held a webinar about this exact topic with Francisco Ramos, Expert Frontend UI Developer at Move Digital AG, and Charles Ahmadzadeh, Co-founder of Bunch.ai. They both gave us their views on if artificial intelligence can design websites and how it is going to fit in frontend and web development (you can watch that webinar here or watch directly below). By the end of this article, you'll know the benefits of artificial intelligence (AI) in frontend development, where it's already being implemented and what the future holds for the technology. Keeping ahead of the curve in the technology industry allows you to better serve your customers, clients or employer. Lucky for you, that's exactly what I'm going to share with you in this article: When will artificial intelligence (AI) replace web development & frontend developers? Let's get into it: Artificial intelligence (AI) is already assisting with the design of websites. Before we get too far into this, I think it's important to say: The most important thing to consider for a website is how easy can the user find the information they're looking for. It's as simple as that. Websites have become more and more complicated as times goes on, but the most important factor is still the user experience. Artificial intelligence (AI) should be used to enhance the user experience or reduce the frontend developers workload, not complicated things. Frontend development is advancing at a quick rate and artificial intelligence (AI) is helping fuel this, especially with making developers more productive. For example: Have you heard of Sketch2Code? This tool from Microsoft claims to "Transform any hands-drawn design into a HTML code with AI." Pretty cool, right? Sketch2Code detects design patterns, understands written text, understands the structure then builds HTML. You can see something very similar in action here: The difference in the GIF above is that this is happening LIVE! I'm sure I don't need to tell you how this could potentially save a lot of time for developers. It could also open up the door for more design-focused people to get involved with web development early on in the process. Does Sketch2Code and other similar tools mean that artists and design-focused individuals are going to start building/designing websites more and more? User experience needs to be considered of course, and there are certain aspects of websites that users have come to expect, but it's still interesting to think about. Artists and designers will certainly have a different idea on how a website should look, which might be perfect for fashion or design brands who are trying to stand out against the competition. Looking at languages and programs that are falling out of favour is an interesting way to see where the technology industry is heading. We decided to take a deep dive into PHP and explore the future of it. You can read that article here or watch the full webinar here. But, back to artificial intelligence, did you know: Artificial intelligence (AI) can help detect bugs too. A group from Microsoft Research, along with Cambridge University, set out to create a model that would detect bugs that no compiler or inter-unit test would be able to find. The DeepCoder is a data generation strategy which models the input-output sets and algorithms for searching over program space - the model is able to write code and learn from a small description of the problem. Here's how it works: You feed the machine a small description of the problem and it will write a few lines of code and try to solve said problem. Though there are some big limitations with this process: the machine can only write in lines of code. Also, there are other experiments that all come with huge limitations, which are not reliable enough to write production work. Pretty interesting stuff, and the model will only continue to improve with huge corporations like Microsoft and Cambridge University working on it. Did you know artificial intelligence plays a major part in healthcare too? Teamed up with blockchain it can be a formidable force. We take a look at this topic in more depth here. Artificial intelligence isn't only limited to web development though: Artificial intelligence is an important part of user experience as well. Let's not forget that artificial intelligence can also be used to enhance the user experience. I'm sure you've all seen one of these pop up at some point in the past week? Chatbots have been integrated into websites for a while now to simplify peoples experience on websites. They can direct people to different areas of the website, answer FAQ's and connect you to the employee who can best help you. A great example of this is Roof AI: The user has received a quick and easy service, and the employee has been sent a lead directly with information attached. What more could you want? Artificial intelligence aiding user experience has a lot of room for growth as well, which we'll talk a little bit more about later on. But on the other hand: AI cannot replace frontend developers in specific tasks. The main question here is whether artificial intelligence could replace frontend developers, which is really asking whether AI could write code. There are a few examples which help put this idea into context: Andrej Karpathy, the director of artificial intelligence and Autopilot Vision at Tesla trained a multi-layer Recurrent Neural Network (RNN). Andrej used an entire Linux repo on Github and compacted the data onto a single, giant file – 500MB of C code to be specific. He then ran a few key functions. With everything in place, including the variable parameter, conditional loop, property indentation, the model functioned adequately. Although he found some mistakes and it didn’t do anything useful, the code did look quite good Pretty good start. Francisco Ramos elaborates on the limitations of the model above created by Andrej Karpathy: "Software developing requires a good understanding of the problem and the business; essentially it requires intuition. We know machines are very good at finding patterns and humans have no chance when competing against machines in infrequent, high-volume tasks but when it comes to solving a problem it hasn’t seen before; machines don’t perform well." Francisco doesn’t think that machines will be able to develop intuition and the ability to interpret business values or features. "I'm certain that machines will evolve to write code one day, although I do not know how reliable this will be." says Francisco. It’s important to not forget that machines will take our code as a reference and will help us along with our workflow. Which means developers will always be needed for that initial input. This further strengthens the idea that AI is able to enhance frontend development, rather than replace it. So, the question remains: What does this mean for frontend developers? It wouldn’t be a bad idea to actually learn about artificial intelligence and machine learning. Francisco says that he has used the framework Tensorflow.js in a few personal projects already. "Tensorflow.js can be used to import models and build and train outside browsers; but it’s also used in building and training, right in the browser using the user GPU." says Francisco. Why would you need machine learning models in the browser instead of in the server-side? "It is GDPR friendly as data never needs the browser; there is no need to send any data to the server (providing better user experience)." says Francisco. Imagine the browser has access to all kinds of sensors today on user input, this would be a great source of user input to play with. You could basically interact with a website by just gesturing, thanks to the library. With Tensorflow.js projects, you don’t need to install a heavy libraries framework, compiler, and interpreters. It’s all in the browser. Here's the deal: Frontend developers should keep an eye out on how the latest technologies are evolving; it is part of the job to be up to date with the evolving technologies - you’ll be replaced quickly if you don’t. The pace it can happen at can sometimes be scary. But you're probably wondering: What is the future of artificial intelligence, frontend and web development? Charles told us "In a couple of years, websites will become even more adaptable and will be able to understand the personalities and emotions of their users when they interact." The website will eventually be able to adapt to these emotions to deliver either a better experience, but also from a business perspective, this will allow for an optimised conversion rate through different funnels. This is crazy. It's also worth noting that a lot of people believe artificial intelligence is going to actually create more jobs instead of replacing humans: In the end, if a company is using certain technology, there should be a reasoning behind it; it shouldn’t just be because artificial intelligence is impressive. This is one of the paths where technology will go, in providing a clear route on investment for a company that would deploy this in their frontend. However, there is a problem that is very frequent and has been yet to be solved. This is the problem of software estimates in predicting bugs and risks. Machine learning could actually be very useful for this problem, in not necessarily replacing predictions but by providing additional data points so that software engineers could make more informed decisions to estimate a user story. If we were able to, we could map out how much code would be needed to be built for a user story and how we’d understand this in the context of the company or the team building the product. Again, this is about tailoring and customising the experience to the user who is using the algorithm in this context. This could actually help developers better estimate, and going beyond estimates, helping with their ability to predict the impact on the maintainability of the codes. I’ve seen a lot of responses saying that artificial intelligence will never replace frontend development. However, there are lots of ideas on where it could go in the future to help frontend developers. Personally, I don’t see a future where developers won’t have a job, but I do see a future where AI will allow them to create better user experiences. In the future, AI will allow developers to have more time to explore other parts in their development process. A company hiring for an artificial intelligence position usually face 4 BIG problems, which I talk more about in this article. Often time is wasted, they're looking in the wrong places or there are issues with ethics. WATCH THE FULL WEBINAR HERE:
AI in Mobility - The Drivery Berlin
In October 2019 we hosted our first AI in Mobility event in partnership with The Drivery in Berlin. This was the first in a series of partnership meetups focussing on this exciting topic. For this event, we welcomed speakers from CIrc annd Carmeq: Presentation One: IQ Sayed, CTO and John Enevoldsen, Data Team Lead @ Circ: How technology and data are shaping the future of micro-mobility. Seemingly overnight Berlin has been taken over by little two-wheelers each coupled with a companion app. Join the experts from Circ (circ.com) , micro-mobility champion in Europe for an up-close personal talk about their current ride launching Circ across Europe and Germany. What was their approach? What were the challenges and lessons learned? What are the predictions for micro-mobility in Berlin? Join us for an evening filled with insights into tech and data insights as well as challenges in the mobility environment and learn how Circ use technology to solve the urban mobility challenge. Presentation Two: Jan Zawadzki, Project Lead, Data & AI @ Carmeq GmbH How Machine Learning is turning the Automotive Industry upside down The automotive industry has mobilized the global economy for decades. German automobile manufacturers (OEMs) alone employ more than 1 million people worldwide and generate sales of more than USD 500 billion. Since a Google + Stanford team won the Darpa Self-Driving Vehicles Challenge 2006 with the help of machine learning, the industry has been undergoing rapid change. Machine learning opens up brand-new business models, from autonomous driving to smart production to personal assistance in the car. However, the use of machine learning requires a different infrastructure than that found in traditional OEMs. Technology-first companies like Waymo or Tesla threaten to overtake established OEMs with billion-dollar market capitalization. Autonomous vehicles produce terabytes of data every day. This data can be immensely valuable in developing machine learning-driven functions. However, substantial challenges remain in the way of using this data. Visit this talk to hear about these challenges to help turn the automotive industry from a mechanical engineering to a software industry. The next event in the series will be taking place in February, so watch this space!
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