"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
Tijana Radivojevic (left) and Hector Garcia Martin working on mechanical and statistical modeling, data visualizations, and metabolic maps at the Agile BioFoundry last year. If you've eaten vegan burgers that taste like meat or used synthetic collagen in your beauty routine – both products that are "grown" in the lab – then you've benefited from synthetic biology. It's a field rife with potential, as it allows scientists to design biological systems to specification, such as engineering a microbe to produce a cancer-fighting agent. Yet conventional methods of bioengineering are slow and laborious, with trial and error being the main approach. Now scientists at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a new tool that adapts machine learning algorithms to the needs of synthetic biology to guide development systematically.
Oracle open-sources Tribuo to fill the gap for enterprise applications focused on machine learning in Java. Committed to deploying machine learning models to large-scale production systems, Oracle has released Tribuo under an Apache 2.0 license. What does Tribuo provide under machine learning? Tools for building and deploying classificationTools for clustering and regression models Unified interface for many popular third-party machine learning librariesA full suite of evaluations for each of the supported prediction tasksData loading pipelines, text processing pipelines, and feature level transformations for operating on dataIn addition to its implementations of Machine Learning algorithms, Tribuo also provides a common interface to popular ML tools on the JVM. Apart from the features mentioned above, Tribuo Model knows when you've given it features it has never seen before, which is particularly useful when working with natural language processing.
Pedro Alves is the founder and CEO of Ople.AI, a software startup that provides an automated machine learning platform to empower business users with predictive analytics. The machine learning and AI-powered tools being deployed in response to COVID-19 arguably improve certain human activities and provide essential insights needed to make certain personal or professional decisions; however, they also highlight a few pervasive challenges faced by both machines and the humans that create them. Nevertheless, the progress seen in AI/machine learning leading up to and during the COVID-19 pandemic cannot be ignored. This global economic and public health crisis brings with it a unique opportunity for updates and innovation in modeling, so long as certain underlying principles are followed. Here are four industry truths (note: this is not an exhaustive list) my colleagues and I have found that matter in any design climate, but especially during a global pandemic climate.
The "Curly" curling robots are capturing hearts around the world. A product of Korea University in Seoul and the Berlin Institute of Technology, the deep reinforcement learning powered bots slide stones along ice in a winter sport that dates to the 16th century. As much as their human-expert-bettering accuracy or technology impresses, a big part of the Curly appeal is how we see the little machines in the physical space: the determined manner in which the thrower advances in the arena, smartly raising its head-like cameras to survey the shiny white curling sheet, gently cradling and rotating a rock to begin delivery, releasing deftly at the hog line as a skip watches from the backline, with our hopes. Artificial intelligence (AI) today delivers everything from soup recipes to stock predictions, but most tech works out-of-sight. More visible are the physical robots of various shapes, sizes and functions that embody the latest AI technologies. These robots have generally been helpful, and now they are also becoming a more entertaining and enjoyable part of our lives.
Before Covid-19 financial institutions saw a 10:1 ratio of bot-based malicious to legitimate login attempts, according to Aite Group's Fraud & AML practice. Malicious login attempts are setting new records every month. Between 2018 and 2019, there was an 84% increase in the number of breached data reports, reaching 15.1B accounts last year. Fraud operations funded by organized crime run much like legitimate businesses, complete with ongoing recruiting campaigns for AI, bot and machine learning expertise and office locations focused on developing breach strategies. As of June 2020, login credentials for online banking averaged about $35 on the dark web while payment card details averaged between $12 and $20 apiece, according to analysis again by Help Net Security.
How AI can support small businesses and self-employed individuals during the pandemic? As the COVID-19 pandemic plays out around the world, consumers, small businesses, self-employed workers and accountants face unprecedented challenges, and these challenges only continue to grow. Many people are struggling to make ends meet and provide for their families. They might be facing a loss of income, a lack of adequate savings to weather the storm or poor access to health care. With shelter-in-place mandates proliferating around the world, small businesses have had to close their doors and are running out of cash to pay their employees and their bills.
I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Humans build knowledge in images. Every time we are presented with an idea or an experience, our brain immediately formulates visual representations of it.
Machine Learning is an important technology for handling data in today's world. It is used to derive models of reality from data. For example, you can use it to segment customer data in an online store or to optimize a performance marketing campaign. This usually requires the use of a programming language with a large number of program libraries for the selected language. Very often "Python" or "R" are used here today and libraries like "Scikit Learn" and "TensorFlow".
In the first part of our tutorial on neural networks, we explained the basic concepts about neural networks, from the math behind them to implementing neural networks in Python without any hidden layers. We showed how to make satisfactory predictions even in case scenarios where we did not use any hidden layers. However, there are several limitations to single-layer neural networks. In this tutorial, we will dive in-depth on the limitations and advantages of using neural networks in machine learning. We will show how to implement neural nets with hidden layers and how these lead to a higher accuracy rate on our predictions, along with implementation samples in Python on Google Colab.