As reported by Nature, a new AI competition will be occurring soon, the MineRL competition, which will encourage AI engineers and coders to create programs capable of learning through observation and example. The test case for these AI systems will be the highly popular crafting and survival video game Minecraft.
Artificial intelligence systems are have seen some recent impressive accomplishments when it comes to video games. Just recently an AI beat out the best human players in the world at the strategy game StarCraft II. However, StarCraft II has definable goals that are easier to break down into coherent steps that an AI can use to train. A much more difficult task is for an AI to learn how to navigate a large, open-world sandbox game like Minecraft. Researchers are aiming to help AI programs learn through observation and example, and if they are successful they could substantially reduce the amount of processing power needed to train an artificial intelligence program.
The participants in the competition will have four days to create an AI that will be tested with Minecraft, taking up to eight million steps to train their AI. The goal of the AI is to find a diamond within the game by digging. Eight million steps of training is a much shorter time span than the amount of time needed to train powerful AI models these days, so the participants in the competition need to engineer methods that drastically improve over current training methods.
The approaches being used by the participants are based on a type of learning called imitation learning. Imitation learning stands in contrast with reinforcement learning, which is a popular method for training sophisticated systems like robotic arms in factories or the AIs capable of beating human players at StarCraft II. The primary drawback to reinforcement learning algorithms is the fact that they require immense computer processing power to train, relying on hundreds or even thousands of computers linked together to learn. By contrast, imitation learning is a much more efficient and less computationally expensive method of training. Imitation learning algorithms endeavor to mimic how humans learn by observation.
William Guss, a PhD candidate in deep-learning theory at Carnegie Mellon University explained to Nature that getting an AI to explore and learn patterns in an environment is a tremendously difficult task, but imitation learning provides the AI with a baseline of knowledge, or good prior assumptions, about the environment. This can make training an AI much quicker in comparison to reinforcement learning.
Minecraft serves as a particularly useful training environment for multiple reasons. One reason is that Minecraft allows players to use simple building blocks to create complex structures and items, and the many steps needed to create these structures serve as tangible markers of progress that researchers can use as metrics. Minecraft is also extremely popular, and because of this, it is comparatively easy to gather training data. The organizers of the MineRL competition recruited many Minecraft players to demonstrate a variety of tasks like creating tools and braking apart blocks. By crowdsourcing the generation of data, researchers were able to capture 60 million examples of actions that could be taken in the game. The researchers gave approximately 1000 hours of video to the competition teams.
Use the knowledge that humans have built up, says Rohin Shah, Ph.D. candidate in computer science at the University of California, Berkeley explained to Nature that this competition is likely the first to focus on using the knowledge that humans have already generated to expedite the training of AI.
Guss and the other researchers are hopeful that the contest could have results with implications beyond Minecraft, giving rise to better imitation learning algorithms and inspiring more people to consider imitation learning as a viable form of training an AI. The research could potentially help create AIs that are better capable of interacting with people in complex, changing environments.
Microsoft to Replace Dozens of Journalists With AI
Microsoft has cut dozens of journalists in what is the latest example of the replacement of human jobs by automation and robots. There are 50 individuals in the United States and 27 in the United Kingdom that will be laid off by June 30th, and their jobs will be replaced by artificial intelligence (AI) software.
The layoffs were not related to the ongoing COVID-19 pandemic. Instead, they are a direct result of the current shift taking place in the economy, one that is replacing human labor with robots and AI technologies.
According to a Microsoft spokesperson, “Like all companies, we evaluate our business on a regular basis. This can result in increased investment in some places and, from time to time, re-deployment in others. These decisions are not the result of the current pandemic.”
The Replaced Team
The individuals that will be replaced are responsible for the news homepages of Microsoft’s MSN website and Edge Browser. The decision comes as Microsoft is currently undergoing a larger reform to use more AI technology in the selection of news.
The 27 individuals in the UK are employed by PA Media, which used to be the Press Association.
One staff member who is part of the team spoke about the transition.
“I spend all my time reading about how automation and AI is going to take all our jobs, and here I am – AI has taken my job.”
AI News Selection
The staffer has concerns about how the technology will handle news selection, since humans followed “very strict editorial guidelines.” These guidelines were in place to prevent violent or inappropriate content from making it to users.
The team was tasked with selecting stories that were produced by other news organizations, and they would edit them to fit a certain format. Microsoft’s website then hosted the articles and shared advertising revenue with the original publishers. The team was also responsible for making sure the headlines were clear and formatted in the correct manner.
According to a spokesperson for PA Media, “We are in the process of winding down the Microsoft team working at PA, and we are doing everything we can to support the individuals concerned. We are proud of the work we have done with Microsoft and know we delivered a high-quality service.”
The replacement of Microsoft’s team is not an isolated incident, the automation of news and journalism is expected to spread.
Just last month, China’s state media announced the newest version of its AI news anchor. It follows the same behaviors and mannerisms of a human anchor, and it can be broadcast to the public.
All of this is taking place as many media organizations are facing financial problems and have resorted to looking elsewhere in order to come up with news stories.
Microsoft in particular has already been implementing AI into their news curation. Over the past few months, the company has been encouraging the use of AI tools in order to scan, process, and filter content.
Ingo Mierswa, Founder & President at RapidMiner, Inc – Interview Series
Ingo Mierswa is the Founder & President at RapidMiner, Inc. RapidMiner brings artificial intelligence to the enterprise through an open and extensible data science platform. Built for analytics teams, RapidMiner unifies the entire data science lifecycle from data prep to machine learning to predictive model deployment. More than 625,000 analytics professionals use RapidMiner products to drive revenue, reduce costs, and avoid risks.
What was your inspiration behind launching RapidMiner?
I had worked in the data science consultancy business for many years and I saw a need for a platform that was more intuitive and approachable for people without a formal education in data science. Many of the existing solutions at the time relied on coding and scripting and they simply were not user-friendly. Furthermore, it made data difficult to manage and maintain the solutions that were developed within those platforms. Basically, I realized that these projects didn’t need to be so difficult so, we started to create the RapidMiner platform to allow anyone to be a great data scientist.
Can you discuss the full transparency governance that is currently being utilized by RapidMiner?
When you can’t explain a model, it’s quite hard to tune, trust and translate. A lot of data science work is the communication of the results to others so that stakeholders can understand how to improve processes. This requires trust and deep understanding. Also, issues with trust and translation can make it very hard to overcome the corporate requirements to get a model into production. We are fighting this battle in a few different ways:
As a visual data science platform, RapidMiner inherently maps out an explanation for all data pipelines and models in a highly consumable format that can be understood by data scientists or non-data scientists. It makes models transparent and helps users in understanding model behavior and evaluating its strengths and weaknesses and detecting potential biases.
In addition, all models created in the platform come with extensive visualizations for the user – typically the user creating the model – to gain model insights, understand model behavior and evaluate model biases.
RapidMiner also provides model explanations – even when in production: For each prediction created by a model, RapidMiner generates and adds the influence factors that have led to or influenced the decisions made by that model in production.
Finally – and this is very important to me personally as I was driving this with our engineering teams a couple of years ago – RapidMiner also provides an extremely powerful model simulator capability, which allows users to simulate and observe the model behavior based on input data provided by the user. Input data can be set and changed very easily, allowing the user to understand the predictive behavior of the models on various hypothetical or real-world cases. The simulator also displays factors that influence the model’s decision. The user – in this case even a business user or domain expert – can understand model behavior, validate the model’s decision against real outcomes or domain knowledge and identify issues. The simulator allows you to simulate the real world and have a look into the future – into your future, in fact.
How does RapidMiner use deep learning?
RapidMiner’s use of deep learning somethings we are very proud of. Deep learning can be very difficult to apply and non-data-scientists often struggle with setting up those networks without expert support. RapidMiner makes this process as simple as possible for users of all types. Deep learning is, for example, part of our Auto machine learning (ML) product called RapidMiner Go. Here the user does not need to know anything about deep learning to make use of those types of sophisticated models. In addition, power users can go deeper and use popular deep learning libraries like Tensorflow, Keras, or DeepLearning4J right from the visual workflows they are building with RapidMiner. This is like playing with building blocks and simplifies the experience for users with fewer data science skills. Through this approach our users can build flexible network architectures with different activation functions and user-defined number of layers and nodes, multiple layers with different numbers of nodes, and choose from different training techniques.
What other type of machine learning is used?
All of them! We offer hundreds of different learning algorithms as part of the RapidMiner platform – everything you can apply in the widely-used data science programming languages Python and R. Among others, RapidMiner offers methods for Naive Bayes, regression such as Generalized Linear Models, clustering such as k-Means, FP-Growth, Decision Trees, Random Forests, Parallelized Deep Learning, and Gradient Boosted Trees. These and many more are all a part of the modeling library of RapidMiner and can be used with a single click.
Can you discuss how the Auto Model knows the optimal values to be used?
RapidMiner AutoModel uses intelligent automation to accelerate everything users do and ensure accurate, sound models are built. This includes instance selection and automatic outlier removal, feature engineering for complex data types such as dates or texts, and full multi-objective automated feature engineering to select the optimal features and construct new ones. Auto Model also includes other data cleaning methods to fix common issues in data such as missing values, data profiling by assessing the quality and value of data columns, data normalization and various other transformations.
Auto Model also extracts data quality meta data – for example, how much a column behaves like an ID or whether there are lots of missing values. This meta data is used in addition to the basic meta data in automating and assisting users in ‘using the optimal values’ and dealing with data quality issues.
For more detail, we’ve mapped it all out in our Auto Model Blueprint. (Image below for extra context)
There are four basic phases where the automation is applied:
– Data prep: Automatic analysis of data to identify common quality problems like correlations, missing values, and stability.
– Automated model selection and optimization, including full validation and performance comparison, that suggests the best machine learning techniques for given data and determines the optimal parameters.
– Model simulation to help determine the specific (prescriptive) actions to take in order to achieve the desired outcome predicted by the model.
– In the model deployment and operations phase, users are shown factors like drift, bias and business impact, automatically with no extra work required.
Computer bias is an issue with any type of AI, are there any controls in place to prevent bias from creeping up in results?
Yes, this is indeed extremely important for ethical data science. The governance features mentioned before ensure that users can always see exactly what data has been used for model building, how it was transformed, and whether there is bias in the data selection. In addition, our features for drift detection are another powerful tool to detect bias. If a model in production demonstrates a lot of drift in the input data, this can be a sign that the world has changed dramatically. However, it can also be an indicator that there was severe bias in the training data. In the future, we are considering to going even one step further and building machine learning models which can be used to detect bias in other models.
Can you discuss the RapidMiner AI Cloud and how it differentiates itself from competing products?
The requirements for a data science project can be large, complex and compute intensive, which is what has made the use of cloud technology such an attractive strategy for data scientists. Unfortunately, the various native cloud-based data science platforms tie you to cloud services and data storage offerings of that particular cloud vendor.
The RapidMiner AI Cloud is simply our cloud service delivery of the RapidMiner platform. The offering can be tailored to any customer’s environment, regardless of their cloud strategy. This is important these days as most businesses’ approach to cloud data management is evolving very quickly in the current climate. Flexibility is really what sets RapidMiner AI Cloud apart. It can run in any cloud service, private cloud stack or in a hybrid setup. We are cloud portable, cloud agnostic, multi-cloud – whatever you prefer to call it.
RapidMiner AI Cloud is also very low hassle, as of course, we offer the ability manage all or part of the deployment for clients so they can focus on running their business with AI, not the other way around. There’s even an on-demand option, which allows you spin up an environment as needed for short projects.
RapidMiner Radoop eliminates some of the complexity behind data science, can you tell us how Radoop benefits developers?
Radoop is mainly for non-developers who want to harness the potential of big data. RapidMiner Radoop executes RapidMiner workflows directly inside Hadoop in a code-free manner. We can also embed the RapidMiner execution engine in Spark so it’s easy to push complete workflows into Spark without the complexity that comes from code-centric approaches.
Would a government entity be able to use RapidMiner to analyze data to predict potential pandemics, similar to how BlueDot operates?
As a general data science and machine learning platform, RapidMiner is meant to streamline and enhance the model creation and management process, no matter what subject matter or domain is at the center of the data science/machine learning problem. While our focus is not on predicting pandemics, with the right data a subject matter expert (like a virologist or epidemiologist, in this case) could use the platform to create a model that could accurately predict pandemics. In fact, many researchers do use RapidMiner – and our platform is free for academic purposes.
Is there anything else that you would like to share about RapidMiner?
Give it a try! You may be surprised how easy data science can be and how much a good platform can improve you and your team’s productivity.
Thank you for this great interviewer, readers who wish to learn more should visit RapidMiner.
Researchers Develop AI Capable of Detecting and Classifying Galaxies
Researchers at UC Santa Cruz have developed Morpheus, a computer program that is capable of analyzing the pixels in astronomical image data. It can then identify and classify all of the galaxies and stars that exist in large data sets that come from astronomy surveys.
What is Morpheus
Morpheus is a deep-learning framework that consists of various different artificial intelligence (AI) technologies. The AI technologies focus on certain applications like image and speech recognition.
Brant Robertson is a professor of astronomy and astrophysics. He is in charge of the Computational Astrophysics Research Group at UC Santa Cruz. According to Robertson, certain tasks that were traditionally done by astronomers need to be automated. This is because the sizes of astronomy data sets are constantly increasing.
“There are some things we simply cannot do as humans, so we have to find ways to use computers to deal with the huge amount of data that will be coming in over the next few years from large astronomical survey projects,” he said.
Ryan Hausen is a computer science graduate student at UCSC’s Baskin School of Engineering. He collaborated on Morpheus with Anderson over the past two years.
Their results were published on May 12 in the Astrophysical Journal Supplement Series. The Morpheus code will also be released to the public and there will be online demonstrations.
Morphologies of Galaxies
Astronomers are able to learn how galaxies form and evolve through time by observing the morphologies of galaxies.
There are some large-scale surveys that are set to take place which will generate massive amounts of image data that can be used. One of those surveys is the Legacy Survey of Space and Time (LSST), and it will be conducted at the Vera Rubin Observatory in Chile.
Robertson has been actively working on ways to use the data to better understand the formation and evolution of galaxies.
When the LSST is conducted, it will take over 800 panoramic images per night with a 3.2 billion pixel camera. Two times each week, the LSST will also record the entire visible sky.
“Imagine if you went to astronomers and asked them to classify billions of objects — how could they possibly do that? Now we’ll be able to automatically classify those objects and use that information to learn about galaxy evolution,” Robertson said.
Deep-Learning Technology for Galaxies
Deep-learning technology has been used by some astronomers to classify galaxies, but it usually requires existing image recognition algorithms to be adapted. The algorithms are traditionally fed curated images of galaxies.
Morpheus was developed specifically for astronomical image data. It uses the original image data, which is in the standard digital format used by astronomers.
According to Robertson, one of the main points of Morpheus is pixel-level classification.
“With other models, you have to know something is there and feed the model an image, and it classifies the entire galaxy at once,” he said. “Morpheus discovers the galaxies for you, and does it pixel by pixel, so it can handle very complicated images, where you might have a spheroidal right next to a disk. For a disk with a central bulge, it classifies the bulge separately. So it’s very powerful.”
The researchers utilized information from a 2015 study in order to train the deep-learning algorithm. The study collected data and classified around 10,000 galaxies in Hubble Space Telescope images from the CANDELS survey. Morpheus was then applied to image data from the Hubble Legacy Fields.
After processing an image of a part of the sky, Morpheus then generates a new set of images of that same area, and it color-codes all objects based on their morphology. Astronomical objects are separated from the background, and it identifies stars and different types of galaxies. The program runs on USCS’s lux supercomputer, where a pixel-by-pixel analysis for the entire data set is quickly generated.
“Morpheus provides detection and morphological classification of astronomical objects at a level of granularity that doesn’t currently exist,” Hausen said.
The work completed by the researchers was supported by NASA and the National Science Foundation.
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