Feedzai, which is based in San Mateo, Calif., and Lisbon, Portugal, received the latest funds from global investment firm KKR, with participation from existing investors Sapphire Ventures and Citi Ventures, according to a March 24 (Wednesday) announcement. With the latest funding round, the company has now raised more than $1 billion from investors to pursue its strategy.Read More
Amazon Forecast is a fully managed service that is based on the same technology used for forecasting at Amazon.com. Forecast uses machine learning (ML) to combine time series data with additional variables to build highly accurate forecasts. Forecast requires no ML experience to get started. You only need to provide historical data and any additional data that may impact forecasts.Read More
Assuring that the huge volumes of data on which many AI applications rely is not biased and complies with restrictive data privacy regulations is a challenge that a new industry is positioning to address: synthetic data production. Synthetic data is computer-generated data that can be used as a substitute for data from the real world.Read More
Amazon today announced the general availability of Lookout for Metrics, a fully managed service that uses machine learning to monitor key factors impacting the health of enterprises. Launched at re: Invent 2020 last December in preview, Lookout for Metrics can now be accessed by most Amazon Web Services (AWS) customers via the AWS console and through supporting partners.Read More
Researchers are bringing together sophisticated algorithms and rich biological data to solve previously intractable problems.Read More
Open source artificial intelligence projects don’t always get a lot of publicity, but they play a vital role in the development of artificial intelligence. Because these open source projects are often pursued as passion projects by developers (sometimes in colleges and universities), the advances are creative and particularly forward-looking.
Typically freed from the constraints of a corporate setting (though some are supported by companies), these open source AI projects can dream big – and often deliver ground-breaking machine learning (ML) and AI advances.
Also important: the advances from these leading open source AI projects fuel the larger AI sector. That is, a new idea from this month’s AI project ends up next year (or even next month) in a high- end AI solution sold by a company.
Remember, if you know of additional top open source AI tools that should be on this list, please include them in the comments section below.
Open Source AI Projects
PyTorch has all the elements you’d expect from a leading open source AI project. It focuses on machine learning, arguably the most popular use of AI in this stage of the emerging technology’s growth. Even more important, developers and AI engineers can set PyTorch up on the top cloud computing platforms; PyTorch on AWS and PyTorch on Azure are both viable, as well as Google Cloud and Alibaba. PyTorch offers neural networks, a foundational element of AI development.
Open Neural Network Exchange
Developed by Microsoft and Facebook, Open Neural Network Exchange offers some very powerful tools, most particularly the ability to recycle fully developed neural network models (which have spent hours and hours being trained in systems) into various other systems. In essence, the Open Neural Network Exchange greatly extends the usefulness of existing models by enabling this porting. Expect ONNX to grow ever more popular in the years ahead.
IBM’s AI Fairness 360
The problem with bias in artificial intelligence algorithms is a growing concern, and AI Fairness 360 is the open source solution to address this. The tool provides algorithms to enable a developer to scan a ML model to find any potential bias, an essential part of fighting bias – and certainly a complex task. Importantly, AI Fairness allows AI engineers to explore the algorithms throughout the development lifecycle. The tool can be set to work automatically. Built into the tool’s foundation is an architecture that checks for correlations; do the correlations create a prediction that suggests a harmful stereotype?
Keras is a rarity in the world of AI open source projects: it promotes itself as “an API designed for human beings, not machines.” A Python deep-learning API, Keras interoperates with high- profile AI projects like Theano and Microsoft Cognitive Toolkit. Developers and AI engineers use it as a ML library to build prototypes with comparative ease. Also aiding its ease of deployment, Keras can run on a mix of processor hardware.
As the name suggests, Accord.NET uses the .NET framework. It’s a .NET ML learning framework that offers image and audio libraries coded in C#. It’s forward-looking, in that it offers a platform for developing commercial-level applications, including apps geared for signal processing, audio-visual toolsets and statistics apps. If you’re just getting your feet wet, Accord also includes template apps so you can start building faster.
Certainly, an open source AI technology that’s generating buzz, Generative Pre-Trained Transformer 2 (GPT-2) was released by OpenAI in 2019. GPT leverages a deep neural network, which uses numerous layers of software to process any number of inputs. GPT-2 is broadly known for handling text, from translation to creating text that, at its best, can be remarkably similar to that written by humans. Moreover, it’s a widely powerful learning tool that can synthesize and adapt to data with significant accuracy.
This project is useful if you’re a ML or AI developer who could use a helping hand with open source ML/AI projects. More of a learning tool than a project, Cheatsheets assists you in getting up to speed with AI/ML projects, from Keras to Scripy to PySpark to Dask. The instruction offered is in-depth and necessarily complex. While Cheatsheets AI is designed for “AI newbies,” in fact you will need some prior training to use this resource.
Is there a developer who doesn’t know TensorFlow? It’s practically a household name. Developed by the Google Brain team for internal use at Google, TensorFlow is now one of the most well-known open source machine learning platforms. Google is also making a cloud-based version of TensorFlow available for free to researchers.
Originally created by the bright minds at UC Berkeley, Caffe has become a very popular deep learning framework. Its claims to fame include expressive architecture, extensible code and speed.
With a huge user base, H2O claims to be “the world’s leading open source deep learning platform.” In addition to the Open Source version, the company also offers a Premium edition with paid support.
Microsoft Cognitive Toolkit
Clearly, Microsoft has moved into the world of open source. Formerly known as CNTK, the Microsoft Cognitive Toolkit promises to train deep-learning algorithms to think like the human brain. It boasts speed, scalability, commercial-grade quality and compatibility with C++ and Python. Microsoft uses it to power the AI features in Skype, Cortana and Bing.
Another very big name in AI and ML. Intended for use in AI research, DeepMind Lab is a 3D game environment. It was created by the DeepMind group at Google and is said to be especially good for deep reinforcement learning research.
Developed at Carnegie Mellon University, ACT-R is the name of both a theory of human cognition and software based on that theory. The software is based on Lisp, and extensive documentation is available. Operating Systems: Windows, Linux, macOS.
StarCraft II API Library
You didn’t think AI was all work, did you? Google’s DeepMind and Blizzard Entertainment are collaborating on a project that makes it possible to use the StarCraft II video game as an AI research platform. It’s a cross-platform C++ library for building scripted bots.
The Numenta organization offers numerous open source projects related to hierarchical temporal memory. Essentially, these projects attempt to create machine intelligence based on current biological understandings of the human neocortex.
A big ambition, to be sure: instead of focusing on a narrow aspect of AI such as deep learning or neural networks, Open Cog aims to create beneficial artificial general intelligence (AGI). The project is working toward creating systems and robots with the capacity for human-like intelligence.
This Java-based natural language processing software can identify the base forms of words, their parts of speech and whether they are names of companies or people, as well as normalizing dates and times. It marks up the structure of sentences in terms of phrases and syntactic dependencies, indicating which noun phrases refer to the same entities, identifying sentiment, extracting particular or open-class relations between entity mentions and getting quotes. It was designed for English but also supports a wide array of languages.
Developed and used by Facebook – yes, they have deep resources – Prophet forecasts time series data. It’s implemented in R or Python and is fully automatic, accurate, fast and tunable.
Originally an IBM Research project, SystemML is now a top-level Apache project. It describes itself as “an optimal workplace for machine learning using big data,” and it integrates with Spark.
Deep learning can be thought of as the furthest edge of AI. Theano, geared for deep learning, describes itself as “a Python library that allows you to define, optimize and evaluate mathematical expressions involving multi-dimensional arrays efficiently.” Key features include GPU support, integration with NumPy, efficient symbolic differentiation, dynamic C code generation and more.
Short for “Machine Learning Language Toolkit,” MALLET includes Java-based tools for statistical natural language processing, document classification, clustering, topic modeling, information extraction and more. It was first created in 2002 by faculty and graduate students at the University of Massachusetts Amherst and the University of Pennsylvania.
An example of cross-collaboration in the open source AI sector, DeepDetect has been used by organizations like Airbus and Microsoft. DeepDetect is an open source deep learning server based on Caffe, TensorFlow and XGBoost. It offers an easy-to-use API for image classification, object detection, and text and numerical data analysis.
What went wrong with artificial intelligence? This transformative technology was supposed to change everything. We’ve seen first-hand the incredible potential it has. So, why has it devolved into overhyped solutions, marketing noise, and an endless spin of the same, tired ideas? Into poor user experiences, embarrassing bugs, and countless other misfires?Read More
According to a report by Market and Markets (Markets & Markets, 2020), “the global Edge AI software market size is expected to grow to USD 1835 million by 2026”. Similarly, a report by 360 Research Reports (360 Research, 2019) estimates that “the global Edge AI Software market size will reach US$ 1087.7 million by 2024”.Read More
Correlation works excellent in simple environments. It works great if you have only a handful of possible causes, AND the effect is following shortly after.Read More
The multifamily industry is presently experiencing a paradigm shift and a revolution with the entry of technological advancements such as artificial intelligence (AI) and demographic shifts. This is disrupting the real estate industry as corroborated by an NMHC “Disruption” report from 2018. Even going through any industry blog, you will most likely come across various mentions of AI. It is a popular topic within the industry and is gaining more traction specifically within the multifamily industry.Read More
Artificial intelligence (AI) is everywhere. Its applications are plentiful and far-reaching, and its growth seems to be pandemic-proof. IDC forecasted that, even in the face of an economic downturn, the AI market would grow by 12.3% in 2020. But there is a notable gap in where AI is taking hold.Read More
Dan Wright just became CEO of DataRobot, a company valued at more than $2.7 billion that is promising to automate the building, deployment, and management of AI models in a way that makes AI accessible to every organization.Read More
New documentation standards in machine learning can enable responsible technology. These risk management strategies highlight how organizations can be compliant while protecting their valuable intellectual property.Read More
Artificial intelligence is a transformative phenomenon in business, government and society. But it is also commonly misunderstood or misrepresented. The Global AI Index is making sense of artificial intelligence in 62 countries around the world. We have examined the forces accelerating development in artificial intelligence through three pillars of analysis; investment, innovation and implementation.Read More
Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which uses omics data to produce ordered lists of >400 drugs based on their anti-proliferative efficacy in cancer cells.Read More
The global machine learning market size is expected to reach USD 96.7 billion by 2025, according to a new report by Grand View Research, Inc. The market is anticipated to expand at a CAGR of 43.8% from 2019 to 2025.Read More
The pandemic has transformed how people work, forcing human resources leaders to bet on AI and other new technologies and processes that support a more adaptive, flexible, and fluid workforce. There have been “seismic shifts” in the way organizations operate, according to Sage’s recent survey of 500 senior HR and people leaders.Read More
Until recently, all of us were learning about software development lifecycle(SDLC). Now, we are at a stage where almost every other organisation is trying to incorporate AI/ML into their product. This new requirement of building ML systems adds/reforms some principles of the SDLC to give rise to a new engineering discipline called MLOps.Read More
Technology is a two-edged sword. On one side, you are assisted by a mammoth amount of applications introduced by a particular technology or a service. While on the other side, you are dealt with the abuse of the same technology. Be it the advent of artificial intelligence, machine learning, the internet of things or the applications of these technologies like online ID verification, digital banking, biometric verification – the ball always falls on both sides of the court.Read More
Artificial intelligence could help identify potential errors in a patient’s medication self-administration method, leading to reduced hospitalizations and healthcare costs, according to a study published in Nature Medicine. A new system uses artificial intelligence to detect errors in patients’ medication self-administration methods.Read More