Tech Fact: The era of big data is here, and it is here to stay. And with that said, along with big data comes an urgent need to integrate new ways of thinking, research, and analytics. Therefore, a much-discussed topic in today’s business world is the various new machine-learning techniques that are already showing their value and will do so increasingly in the future. But machine learning, like any other disruptive technology, is a complex concept overall. As a business owner, manager, or user, you need to be aware of current and future trends to harness this innovative technology’s true potential and power.
But first things first…What is Machine Learning?
Machine learning is a branch of artificial intelligence (AI) which refers to the ability of computers to learn from data and make decisions based on that knowledge. This can be applied in many different areas, including marketing, healthcare and finance. ML involves feeding large amounts of information into a machine to learn from over time. The more data you provide in your ML algorithm, the better results you’ll get when predicting future outcomes based on past experiences with similar situations or objects. So far, the tech industry is already seeing a boom in machine learning, and it will only get bigger in the coming years. We will see more jobs, businesses, and innovation happening at an accelerated pace.
How can we use Machine Learning to our benefit?
Machine learning is a powerful tool that can be used to solve problems that humans can’t solve. It can help you make decisions, predict future behaviour, and improve systems. Let’s look at some examples of how machine learning can be used in your life:
You’re planning a trip somewhere new with friends and family. How would you use machine learning tools like Google Maps or Waze to get there without having an accident? Machine learning uses algorithms to analyze past data (like weather conditions) for patterns that may impact driving conditions or safety on the roadways ahead; by using these tools before leaving home, you’ll know what kind of weather will be present when travelling so maybe don’t pack those heavy winter coats just yet!
It can help you pick your next song on Spotify by analyzing your listening habits (and maybe even predicting how long it’ll take for you to stop playing one song).
Other common examples of today’s reality are that of a self-driving car which uses machine learning algorithms like deep neural networks (DNNs) and convolutional neural nets (CNNs) to identify road signs and avoid accidents. ML algorithms are also increasingly used in healthcare settings, such as facial recognition technology at hospitals or diagnostic imaging systems, which can detect cancerous growths more accurately than doctors alone.
Machine Learning Day December 2022 – WeAreDevelopers
WeAreDevelopers: It is a fact that machine learning is one of the best ways to make your data more powerful for business users. It offers a broad range of applications, from helping us understand our world better to forecasting trends and predicting what might happen next. By using machine learning technologies in conjunction with other disciplines like statistics or economics, your company can be ready for tomorrow’s challenges today.
While trying to understand this innovative and emerging technology better, I attended a few days ago an online webinar organized by WeAreDevelopers, called Machine Learning Day. This webinar included topics of the moment regarding ML, explained by world-leading experts regarding this technology. Here is a quick recap of the speakers invited and their exciting topics.
Machine Learning Speakers at the WeAreDevelopers Webinar
Nils Murzyn, Machine Learning Researcher at ZF Friedrichshafen AG: Nils came to this webinar with an exciting talk explaining one of the main issues regarding advanced driver-assistant systems and automated driving functions is the assessment of interactions between traffic participants. This session presented how ZF uses such algorithms to anticipate the future behaviour of traffic participants and thus improve comfort and road safety.
Mihailo Joksimovic, Senior Software Engineer at Microsoft: Mihailo introduced the term Recommendation Systems during this webinar, or decision-making assistants. He covered the basics of Math, then proceeded with a brief overview of Classification, and discussed how we could use Matrix Factorization techniques to build state-of-the-art Rec. Systems.
Miguel Martínez & Meriem Bendris, Senior Deep Learning Data Scientist & Senior Solution Architect at AI at NVIDIA: Miguel and Meriem delivered a wonderful technical overview of the end-to-end process for building large-scale language models (a reference to the NLP and GPT3 concepts of natural language processing), and described some existing solutions for efficient data preparation and distributed training.
Matthias Niehoff, Head of Data at codecentric: Matthias, in this webinar, thoroughly explained what parts of a CI/CD pipeline for Machine Learning are needed – and which are optional. Also, how can the whole thing be implemented without building an entire Machine Learning Platform team? Some challenges still need to be solved.
Ghada Alzamzmi, Research Scientist at National Institutes of Health: Ghada concluded this webinar by introducing AI-based technologies that can be used to monitor health and predict complications of vulnerable patient populations, including infants and minority groups. Also, she explained some pressing challenges and future directions for AI-enabled wearable technology in healthcare.
This webinar could take you on an exciting learning journey regarding machine learning techniques, innovations, uses and benefits, and the challenges ahead. This webinar is free and public for anyone who wants to leverage their knowledge and better understand what machine learning consists of. If you are a tech enthusiast and want to learn more about machine learning, this is a great start to jump right into the pool of knowledge. Click here to see the entire webinar.
