Explore these AI tools
Practical artificial intelligence has made its way out of the labs and into our daily lives. And judging from the pace of activity in the startup community and the major IT powerhouses, it will only grow in its ability to help us all get things done.
Most AI solutions today are fielded by the big players in IT. For example, Apple’s Siri or the capabilities Apple embedded directly in iOS9, Google’s many savvy search solutions, Amazon’s very smart recommendation engine, and IBM’s Watson.
IBM Watson - Find your Celebrity match.
IBM Chef Watson - create your own menu.
Google Doodle Recogniser
AI Personal Assistant
Your AI Replica
A short list of AI applications for you to explore
On your phone:
- Siri: Part of Apple’s iOS, watchOS, and tvOS. Intelligent personal assistant.
- Cortana: Microsoft’s intelligent personal assistant. Designed for Windows Mobile but now on Android, and a limited version runs on Apple iOS. Also works on desktops and Xbox One.
- Google Now: Available within Google Search mobile app for Android and iOS as well as the Google Chrome web browser on other devices. Delegates requests to web services powered by Google.
Really, most all of the AI on your phone and desktops is communicating with cloud services, so keep in mind that most solutions are a blend that highly leverages cloud capabilities. But we needed a place to talk about Echo and Watson and soon many others, so:
- Watson: A technology platform that uses natural language processing and machine learning to reveal insights from large amounts of data.
- Echo: The device you buy is mostly a speaker and microphone with some commodity IT to connect it to the cloud. The real smarts come from Amazon Web Services.
- Arria: Natural Language Generation
For personal and business use:
- Gluru: Organize your online documents, calendars, emails and other data and have AI present you with new insights and actionable information.
- x.ai: Let AI coordinate schedules for you. Your own personal scheduler.
- CrystalKnows: Using AI to help you know the best way to communicate with others.
- RecordedFuture: Leverages natural language processing at massive scale in real time to collect and understand more than 700,000 web sources.
- Tamr: Unique approaches to Big Data, leveraging machine learning.
- LegalRobot: Automating legal document review in ways that can serve people and businesses.
- Kitset: Make your own apps
- Vicarious: Building the next generation of AI algorithms.
- Soar: a general cognitive architecture for developing systems that exhibit intelligent behavior.
- Prediction.io: a service with easy to use, open templates for a variety of advanced AI workloads.
- Jade: Java Agent Development Framework. Simplifies multi-agent system development.
- Protege: A free, open-source ontology editor and framework for building intelligent systems.
- h2o.ai: Build smarter machine learning/AI applications that are fast and scalable.
- Seldon: An open, enterprise-grade machine learning platform that adds intelligence to organizations.
- SigOpt: Run experiments and make better products with less trial and error.
- Scaled Inference: Cloud based models and an inference engine to help in model selection.
- OpenCV: Open-source computer vision, a library of programming functions aimed mainly at computer vision.
- OpenCog: An open-source software project whose aim is to create an open-source framework for artificial general intelligence (AGI).
- TensorflowGoogleGoogle’s ability to promote their machine learning framework and fund it ensure that adoption is going nowhere but up. Nobody has ever been fired for choosing Tensorflow.
- Caffe Not-for-profitSupported by grants from Nvidia and Amazon. Caffe can process over 60 million images per day with a single NVIDIA GPU.
- Torch Not-for-profit Used within Facebook, Google, Twitter. Computing framework for machine learning and computer vision. Originally developed by academics at NYU.
- Theano Not-for-profit A Python library that allows you to evaluate mathematical expressions involving multi-dimensional arrays. Whatever that means.
- scikit Not-for-profit Machine learning in Python. If you want to see lots of cool examples of what can be done with scikit, just click here.
- H2OH2O.ai World’s leading deep learning platform used by 80,000 data scientists in 9,000 organizations. Used by 20% of Fortune 500 companies. $33.6 million in funding.
- Neon Nervana Systems The “world’s fastest deep learning framework”. Acquired by Intel in August of last year for $350 million. They’ll up-sell you to use their cloud once you build your app on their platform.
- Deeplearning4j SkymindStartup which took a $3 million seed in Fall of 2016. Tencent lead investors. Deep learning library for Java that wants to be the “Red Hat of deep learning”.
- CNTK Microsoft Microsoft’s “cognitive toolkit” that trains and evaluates deep learning algorithms with language like C++ and Python. Fast and scalable. Optimized for their cloud platform Azure.
- Mahout Apache Software Quickly create scalable machine learning applications. Core algorithms for clustering, collaborative filtering, and classification. Improved sales from Amazon recommendation engine by 35%.
- MLlib Apache Software MLlib is Apache Spark’s scalable machine learning library. The tagline reads “lightening-fast cluster computing” and speed seems to be one of their key selling points.
- NuPIC Numenta CB Insights AI 100. Silicon valley startup. Framework inspired by neuroscience called “Hierarchical Temporal Memory” or HTM. Invested in Cortical.io. Biologically inspired machine intelligence.
- OpenNN Artelnics An open source class library written in C++ programming language which implements neural networks. Developed by a Spanish startup called Artelnics.
- PredictionIOSalesforce Described as “MySQL for machine learning”. Open source machine learning platform for building predictive applications as the name implies.
- SeldonSeldonLondon startup founded by data scientists. Full-stack open-source machine learning solution for recommendation engines. Serves billions of predictions a month.
- Enlitic: Deep learning for healthcare and data-driven medicine.
- Metamind.io: Automatic image recognition with many use cases, including medicine.
- Zebra Medical Vision: Closing the gaps between research and result for patients with data and AI.
- Deep Genomics: Machine learning and AI transforming precision medicine, genetic testing, diagnostics and therapies.
- Atomwise: Using AI and analytics to predict medicines and discover drugs.
- Flatiron.com: AI and machine learning delivering insights on treatments.
- Mttr.net: Building flying vehicles powered by intelligent software.
- Skycatch: Software for fully autonomous aerial systems.
- SpaceKnow: Using AI to track global economic trends from Space.
- OrbitalInsight: Space trends for understanding global issues.
For marketing and customer interaction:
- DigitalGenius: Computer driven conversation with customers in ways that scale and serve.
- Conversica: AI to help you find your next customer, including automated email conversations to qualify leads.
- AI-CMO: New Zealand agency supporting growing companies with modern marketing technology and methodology solutions.
Emarsys - Artificial Intelligence Marketing.
Gluru: Organise your online documents, calendars, emails and other data and have AI present you with new insights and actionable information.
Narrative Science: Transform Business and Marketing Data into Machine-Written Narratives with Advanced Natural Language Generation
Albert.ai - Intelligent Media Buying
OneSpot: Automatically Personalize Content Across Channels with Machine Learning and Natural Language Processing
Automated Insights: How the Associated Press and the Orlando Magic Write Thousands of Pieces of Content in Seconds
Cortex: Predict How Consumers Will React to Content Using Machine Learning
Boomtrain: This Machine Learning Technology Displays the Content Most Likely to Engage Prospective Customers
Scoop.it: Produce and Promote Better Content in Less Time with Artificial Intelligence
MarketMuse: How to Measure and Improve Content Quality at Scale
Curata: Manage Data-Driven Content at Scale
Scripted: Streamline the Content Marketing Process with Machine Learning
Adgorithms: Optimize Your Multi-Channel Marketing Campaigns with an AI Platform
CaliberMind: Predict B2B Behavior with Artificial Intelligence
PaveAI: Turn Google Analytics Data Into Actionable Recommendations with AI
Opentopic: Make Marketing More Personalized and Predictive with IBM Watson-Powered Technology
Atomic Reach: Use Machine Learning to Score and Improve Your Content Before Publishing
Skyword: Employ Artificial Intelligence to Hyper-Personalize Enterprise Content
Crayon: This Artificial Intelligence Startup with $5 Million in Funding Will Tell You Exactly What Your Competitors Are Doing
Acrolinx: This Company Uses Artificial Intelligence to Improve Content for Facebook, IBM and Nestle
CaliberMind: B2B Buyer Journey Orchestration software
MonkeyLearn: MonkeyLearn uses machine learning to allow users to build customized text-analysis models.
Persado - Cognitive Content Platform: Persado is a cognitive content platform for marketers. Persado's products and technology generates language that inspires action and increases ROI.
Big Blue was one of the early pioneers of artificial intelligence, and introduced the masses to modern AI when its Watson system took part in the television game show Jeopardy. It generally refers to its AI solutions with the term "cognitive computing," and it sells them under the brand name "Watson." It has dozens of different AI products and services available, and they generally fall into two categories: developer tools and premade applications that use Watson technology. The company is also sponsoring a $5 million competition that challenges startups to use AI to "tackle some of the world's grand challenges."
1. Watson APIs
Designed for developers, these tools allow other companies to utilize Watson cognitive computing capabilities in their own apps. It currently offers about 19 different APIs with capabilities like concept expansion, conversation, language translation, personality insights, tone analyzer, relationship extraction, speech to text, text to speech, visual recognition and analytics. They can be accessed through the IBM Watson Developer Cloud service.
In the Watson Marketplace, IBM offers applications it has built that are based on its cognitive computing technology. These include Watson Trend (a personal shopper app), Watson Analytics, Talent Insights, Analytics for Social Media and Watson for Clinical Trial Matching (for the healthcare industry).
IBM has open sourced some of its machine learning technology, including SystemML. Now an Apache Incubator Project, SystemML "aims at flexible specification of ML algorithms and automatic generation of hybrid runtime plans ranging from single node, in-memory computations to distributed computations on MapReduce or Spark."
Known for dedicating a lot of resources to research, Google has an internal team calledGoogle Brainthat works on AI projects. Much of their work gets applied to Google's other products, including search and the Google Now Android personal assistant. It has also released some of their team's work as open source applications, and the group has published quite a few papers on AI.
One of Google's open source AI projects created by the Google Brain Team, Tensor Flow is a "library for numerical computation using data flow graphs." The website includes Python and C++ APIs that allow developers to use Google's AI capabilities in their own apps.
Google makes some of its machine learning technology available to developers through its Google Cloud platform. It uses the same services for many of its own products, including Photos image search, Google voice search, Translate and Gmail smart reply.
In 2014, Google bought a London-based AI startup called DeepMind. This group's most visible work to date has been creating the AlphaGo system, which was "the first computer program to ever beat a professional player at the game of Go." The team is also working on applying reinforcement learning to machine learning and applying deep learning technology to the field of healthcare.
Google doesn't provide a lot of details about the factors that contribute to rankings on its search engine, but it has said that it uses an AI technology called RankBrain as part of the algorithm. RankBrain can guess at the meaning of search terms it has never seen before and bring up relevant results. It isn't available for download or for sale but is the subject of much interest within the technology industry.
Like the other large technology companies, Microsoft has a sizable internal team devoted to machine learning and artificial intelligence. It has subgroups focused on algorithmic economics, deep learning, machine learning, machine teaching, natural language computing and more, and it has a long list of current projects on its website. Their innovations have also been integrated into other Microsoft products and services.
Microsoft's personal assistant software, known as Cortana, is perhaps its most visible AI product. It has been incorporated into Windows 10 and is also available for Android and iOS. It can perform tasks like providing updates, delivering reminders and handling natural language searches.
In recent years, Microsoft has begun embracing open source, and it has released some of its AI technology on GitHub. CNTK, short for Computational Network Toolkit, is a tool that allows developers to apply distributed deep learning to their own projects. It was recently updated to enable faster performance and better scalability.
Another open source project, the Distributed Machine Learning Toolkit (DMLT) assists with the training of big models for machine learning applications. It includes the DMTK Framework, the Light LDA topic model algorithm and the Distributed (Multisense) Word Embedding algorithm.
Microsoft also offers developers several AI APIs on a subscription basis, with free tiers available. Current APIs include Computer Vision, Emotion, Face, Video, Bing Speech, Language Understanding, Knowledge Exploration, Recommendations and more. Microsoft has also used these APIs to build several sample applications, some of which have gone viral in social media.
12. Project Malmo
In this interesting project, Microsoft researchers are introducing AI to the game Minecraft. They are working to teach the AI how to make sense of complex environments, learn from others and transfer learned skills to new problem-solving challenges. It's currently a private beta that Microsoft plans to release under an open source license.
In March 2016, Microsoft set loose an AI chatterbot named Tay that was designed to interact and learn from people on Twitter. In less than a day, other Twitter users had taught Tay to make racist and offensive comments, and Microsoft took it offline. It re-released Tay a week later only to encounter similar problems. The company says it plans to put Tay back on Twitter "once it can make the bot safe."
Facebook - Wit.ai
The social network has invested heavily in artificial intelligence, primarily through an internal group it calls. Facebook AI Research (FAIR). Much of this research into fields like natural language processing and computer vision gets applied directly to Facebook itself through features like face tagging and newsfeed rankings. The group has also published several papers and contributes to open source AI projects
Purchased by Facebook in 2015, Wit.ai offers developer tools for building bots that communicate with humans. Its voice recognition technology can also be used for interacting with mobile apps, home automation, wearables or even robots.
Another heavy investor in AI, Amazon has long used machine learning on its ecommerce site to make product recommendations and predict prices. And its CEO Jeff Bezos recently said, "It's hard to overstate how big of an impact [AI is] going to have on society over the next 20 years." In addition, the company recently purchased an AI startup known as Orbeus.
Alexa is the technology behind Amazon's Echo device that allows users to play music, get answers to questions, buy products and more. The company recently opened up the Alexa technology to developers who can use it to power their own apps and devices.
Amazon's cloud computing division, Amazon Web Services, offers an AI service called Amazon Machine Learning. A free tier is available for developers who want to experiment with the service, which easily scales as necessary.
Apple's has been somewhat more tight-lipped about its AI plans than some of the other big technology firms, but there's no doubt it is investing in the area. It has several job postings related to machine learning, and it recently purchased AI startups Emotient and Vocal IQ.
The most visible fruit of Apple's AI efforts is its voice assistant Siri that comes installed on iOS devices. Although it has taken some criticism, this early personal assistant set the bar for similar AI-powered assistants like Cortana and Google Now.
Intel's AI efforts have largely focused on enabling machine learning, deep learning and AI with its chips and software. It has also acquired quite a few smaller companies focused on AI.
Intel bought Saffron in October 2015. It offers two applications: Streamline uses cognitive technology to speed the development of new products, and Advantage performs visual analytics on big data.
Yahoo CEO Marissa Mayer has spoken out about the importance of AI, and the company has developed several AI tools internally to help run its various websites.
19. Caffe on Spark
This year, Yahoo open sourced a tool called Caffe on Spark that brings together two well-known open source projects (Caffe and Spark). Essentially, the project makes it possibly to perform machine learning on large Hadoop clusters, and Yahoo uses it to help run its Flickr photo service.
Acquisitions have also helped Yahoo expand its AI capabilities. In 2013, it bough SkyPhrase, and it now offers SkyPhrase natural language capabilities to developers as an SDK. However, note that developers need to request an invite in order to get access to the technology.
Like many other tech leaders, Marc Benioff, CEO of Salesforce, is enthusiastic about AI, predicting an "AI-first world." The company has been bolstering its AI capabilities by gobbling up smaller firms.
Bought by Salesforce in April of this year, MetaMind aims to bring deep learning and artificial intelligence to business applications. MetaMind's standalone services are being phased out as the technology becomes integrated into Salesforce's cloud computing offerings.
PredictionIO became part of Salesforce in February 2016. It offers open source machine learning servers that developers can use to create prediction engines very quickly. Salesforce is also integrating the technology into some of its products.
This open source machine learning framework makes it easy to add audio or image processing capabilities to an application. The website includes resources like sample applications, documentation and a wiki to help developers get up to speed on the technology very quickly.
24. Apace Mahout
The stated goal of this open source project is "to build an environment for quickly creating scalable performant machine learning applications." It includes three key pieces: a programming environment for developers who are building AI-powered applications, premade algorithms for a variety of tools and a vector math experimentation environment called Samsara.
Short for "Brain artificial," Braina is a commercial personal assistant app for Android or Windows PCs. It has the ability to learn from information that you tell it as well as performing simple tasks on your smartphone or computer.
Developed at the University of California Berkeley and the Berkeley Vision and Learning Center, Caffe is an open source framework for deep learning. It boasts expressive architecture, extensible code, fast performance and an active community of users and developers.
The Cycorp company offers several different semantic tools under the Cyc brand name. OpenCyc is an open source knowledge base and reasoning engine; EnterpriseCyc is a commercially supported implementation of the same technology; and ResearchCyc is a free implementation for AI researchers.
This open source tool brings commercial-grade deep learning capabilities to Java. It integrates with big data tools like Hadoop and Spark, and commercial support is available through Skymind.
Encog is an open source machine learning framework that supports artificial neural networks, support vector machines, bayesian networks, hidden markov models, genetic programming and genetic algorithms. Available for Java or C#, it's a cross-platform tool that works well on multicore, GPU-equipped hardware.
This enterprise-focused AI company counts Capital One, Cisco, Nielsen Catalina, PayPal and Transamerica among its users. It offers tools for using machine learning capabilities with big data tools like Spark, Hadoop and R, and it has both open source and commercially supported products.
The Apache Spark large-scale data processing engine has a machine learning library called MLlib. It promises easy deployment on Hadoop with 100 times faster performance than MapReduce.
This company is dedicated to creating open source technology that uses AI to control the Internet of Things (IoT). They have released several open source natural language processing tools, and they have a crowdfunded IoT control device that looks like a very friendly robot.
Neuroph is an open source Java-based framework for developing neural network architectures. It's designed to be used by developers who are new to AI, offering quite a bit of online documentation.
Numenta is a company developing products based on a theory called Hierarchical Temporal Memory, which offers a framework for both biological and machine intelligence. NuPic is its open source platform based on this theory which can be used for data analysis, prediction and anomaly detection.
36. Open Cog
Another open source initiative, Open Cog is dedicated to "creating beneficial artificial general intelligence (AGI), with broad capabilities at the human level and ultimately beyond." The technology is currently in use at Hong Kong Polytechnic University, and the team is confident that they will soon have software capable of human preschool-level intelligence.
37. Oryx 2
Based on the architecture of Apache Spark and Apache Kafka, Oryx 2 is an application development framework specifically designed for real-time, large-scale machine learning. It's an open source project created by Cloudera.
Short for "Open Neural Networks," OpenNN is a predictive analytics library written in C++ that boasts high performance. It was developed by Artelnics, a software developer that specializes in creating data analysis software for enterprises.
This open source project offers machine learning tools for Python, with a focus on data mining and analysis. It builds on the work of several other open source projects, including NumPy, SciPy, and matplotlib.
Shogun describes itself as "a large-scale, machine-learning toolbox." It supports a wide variety of programming languages and offers classification, regression, dimensionality reduction, clustering, metric, multi-task, structured output, online learning feature hashing, ensemble methods and optimization capabilities.
According to its website, Theano has been "powering large-scale computationally intensive scientific investigations since 2007." It's a Python library for working with mathematical expressions involving multi-dimensional arrays efficiently, and it is useful for some deep learning applications.
Built to run on GPUs and based on LuaJIT, Torch is an open source scientific computing framework that supports a lot of machine learning algorithms. Community members have created Torch packages for machine learning, computer vision, signal processing, parallel processing and other AI applications.
Created by the team of developers behind Siri, Viv is a new AI platform controlled by conversational input. It learns constantly from the world around it, allowing it to expand its capabilities on a daily basis. The software isn't yet available for download, but the company behind it is currently seeking partners who are interested in integrating Viv into their own products.
Created by the machine learning group at the University of Waikato in New Zealand, WEKA enables data mining in Java applications. It includes machine learning algorithms for data pre-processing, classification, regression, clustering, association rules and visualization.
This commercial project is a knowledge engine that can answer questions on a huge variety of subjects, including math, languages, chemistry, dates, health, science, money, history and much more. Anyone can use the free version on the website above, or you can subscribe to the Pro service for around five or six dollars a month.