Machine learning (ML) is a sector that is both growing exponentially right along with ML’s twin, artificial intelligence (AI). IDC predicts that AI and ML spending will explode in the coming years, from $8 billion in 2016 to $47 billion by 2020.
While the two terms are used interchangeably, and often together, there is a difference between the two. AI is a large umbrella of automation, while machine learning is a subset of AI that involves a program or application gaining better knowledge or understanding of the task it is performing, based on data, without requiring it to be reprogrammed.
AI/Machine Learning Resources:
- Building the Right Environment to Support Machine Learning
- Forrester Wave: Machine Learning Data Catalogs
- AI for Executives: Integrating AI into Your Analytics Strategy
- Harvard Business Review: The Risks and Rewards of AI
- Making Sense of AI
- The Artificial Intelligence of Things
IDC also predicts that 40% of digital transformation initiatives will be supported by AI and ML by end of 2019, so it’s not hard to see why these buzzwords are being thrown around so frequently. Everyone wants to hop on the bandwagon.
For too long, paradigm shifts were led by disruptive start-up firms, and the old guard was usually left behind because it was unwilling to disrupt its hegemony or too slow to respond. The concept of software-as-a-service was driven by Salesforce, not IBM or Oracle, although the two did get religion quick.
In the case of ML, though, the effort is being led by the old guard as well as the new guard. Established players have learned to be more nimble and not cling to their old ways because it was profitable until it was too late. That’s why many of the most innovative machine learning firms are established players.
What follows is a mix of 15 top machine learning firms, selected because of the significance of their offerings. The list is in alphabetical order, not order of rank or perceived importance.
Top Machine Learning Companies
Sure this list of machine learning companies will evolve rapidly. If you have suggestions for additions, please use the Comments section below.
Machine learning is used all along the length of Amazon consumer services, starting with its online store to Kindle and Echo devices. Machine learning is used to determine user preferences things like product purchases, as well as for Alexa Engine, Alexa Smart Home Devices, Amazon JHIM, Amazon Rekognition, Amazon Music, and other features. In addition, the company offers machine learning services through AWS built on experience gained with consumer products.
Apple has greatly enhanced Siri with machine learning so it can do more than call someone in your contacts list. It can now identify someone who recently emailed you but is not in your contact list, for example, as well as facial recognition, recognized more than 30,000 Chinese characters, or show you where you parked your car.
Apple has acquired a number of machine learning and AI startups in the past two years – Lattice.io, Regaind, Pop Up Archive, Init.ai and SensoMotoric, just to name a few.
Originally a DARPA-funded startup, Ayasdi was born out of Stanford¹s mathematics department. Its core technology, Topological Data Analysis, finds subtle patterns in complex data, in particular the ability to find insight in what it calls “dark data,” or data that was often considered to be useless but actually holds tremendous value.
4. Digital Reasoning
Digital Reasoning specializes in cognitive computing to apply machine learning identifying interesting human behaviors in communications data. It uses AI to accumulate context and fill in the understanding gap from any source, resolve what’s valuable and what’s not, and draw conclusions based on exposing concealed relationships, risks and opportunities.
Darktrace uses AI and machine learning to offer cybersecurity systems called Enterprise Immune System, which mimics the human immune system by learning what is ‘normal’ for all devices and users, updating its understanding as the environment changes, and then looks for abnormalities that could indicate security issues. Because of this it does not require the virus signatures database traditional antivirus software uses and has to constantly update as new threats are found.
Dataiku offers analytics software that enables companies to build and deliver their own data products more efficiently. Dataiku’s Data Science Studio is an enterprise-grade platform for data teams that enables companies to build and deliver apps and projects using their own data more efficiently. It’s meant to help data scientists be a part of the greater company team in everything from security to marketing campaigns.
Machine learning is used every day by every one of Facebook’s two billion users without them realizing it. It’s used for friend tagging suggestions, personalized news feed, mutual friend analysis and group recommendations for Facebook, Messenger and Instagram. The company has four AI research campuses around the world, reflecting its focus on AI for running the site.
Feedzai was founded by data scientists and aerospace engineers with the goal of providing end-to-end fraud prevention and offer consumers a better and safer experience, all through AI and ML. It supports online, mobile and brick and mortar stores, and rather than work from rules and patterns, machine learning acquires knowledge for every sale. As a new channel comes online for a customer, it automatically begins monitoring that channel. It lets analysts predict and prevent electronic payment loss in real time based on behavioral analysis.
Google has been on a tear, acquiring 13 companies in the past five years, to enhance visual processing, image processing, Google language, search engine ranking, speech recognition, and search prediction capabilities. In addition, it offers the Cloud AI service to customers of its Google Cloud services, allowing customers to add machine learning to their applications for things like image search and recognition, translation and voice control.
10. IBM Watson
Watson has been around a few years but the machine learning aspect just launched last year. It allows data scientists to transform data and apply machine learning algorithms to train predictive models and build intelligent applications that leverage the predictions generated by machine learning models. Developers can also apply an algorithm to learn from data sets to generate models that can make predictions based on the data set. It also offers data model building for customers, who can choose from algorithms IBM provides or let IBM decide which is best for them.
Luminoso is one of the top artificial intelligence companies specializing in natural language understanding software. It plows through unstructured text data, from call center and chatbot transcripts to social media posts, to help companies gain insights from the conversations and feedback, as well as optimize their client interactions, detect customer trends, and discover what issues matter to customers.
N-iX is something of a custom development shop, specializing in machine learning & cognitive computing expertise. It has more than 800 engineers in-house building custom apps for customers in Healthcare, Fintech, Aviation, Information and Content Management, Entertainment, and other industries. It creates machine learning algorithms in Python & R and uses multiple additional libraries, like Caffe, DeepLearning4J, TensorFlow, Theano, Torch, and more.
Pioneer among machine learning companies and artificial Intelligence companies. They apply machine learning to make data-driven decisions at a speed demanded by your business. Multidimensional problems that cannot be easily analyzed by the human brain can be resolved using a wide range of machine learning techniques. By identifying latent structures in data, revealing new insights, and making accurate predictions from data, machine learning algorithms can contextualize the information contained in huge datasets. Leveraging machine learning, you can optimize information-centric business processes, customize solutions per customer requirement, drive productivity, forecast demand, among a host of other possibilities.
Qualcomm hasn’t been as acquisitive as its competitors like Apple, Samsung and Intel, but it made a major move into ML with the 2017 acquisition of Dutch machine learning startup Scyfer for an undisclosed amount. Qualcomm efforts have centered around deploying artificial intelligence technology at the device level. Most of its newer Snapdragon chips have some kind of AI features designed to run on the phone, not send the computation up to the cloud. Qualcomm said it is focusing on on-device solutions to enhance reliability, cut latency and bandwidth usage and improve privacy protections. Scyfer has built AI tools for functions like revenue prediction, sound recognition for healthcare and quality inspection for manufacturing companies.
If you’re going to call yourself “The Machine Learning Company,” you better back it up. Skytree offers an enterprise-class machine learning platform that help customers discover deep analytic insights, predict future trends, make recommendations and reveal untapped markets and customers. The Skytree machine-learning platform is designed to continuously search for the most accurate models to continuously improve the performance of the models.
To the outside world, Uber is a ride-hailing business. Behind it is Michelangelo, a machine learning-as-a-service platform that enables internal teams to seamlessly build, deploy, and operate machine learning solutions at Uber’s scale. It covers end-to-end ML workflow, such as managing data, training, evaluating, and deploying models, making predictions, and monitoring predictions, such as how soon before your ride will arrive. Uber plans to eventually offer this ML-as-a-service to the public.