Top 25 Machine Learning Startups To Watch In 2021 Based On Crunchbase

Top 25 Machine Learning Startups To Watch In 2021 Based On Crunchbase

  • There are a record number of 9,977 machine learning startups and companies in Crunchbase today, an 8.2% increase over the 9,216 startups listed in 2020 and a 14.6% increase over the 8,705 listed in 2019.
  • Artificial Intelligence (A.I.) and machine learning (ML)-related companies received a record $27.6 billion in funding in 2020, according to Crunchbase.
  • Of those A.I. and machine learning startups receiving funding since January 1, 2020, 62% are seed rounds, 31% early-stage venture rounds and 6.7% late-stage venture capital-funded rounds.
  • A.I. and machine learning startups’ median funding round was $4.4 million and the average was $29.8 million in 2020, according to Crunchbase.

Throughout 2020, venture capital firms continued expanding into new global markets, with London, New York, Tel Aviv, Toronto, Boston, Seattle and Singapore startups receiving increased funding. Out of the 79 most popular A.I. & ML startup locations, 15 are in the San Francisco Bay Area, making that region home to 19% of startups who received funding in the last year.

Israel’s Tel Aviv region has 37 startups who received venture funding over the last year, including those launched in Herzliya, a region of the city known for its robust startup and entrepreneurial culture.  Please see the Roundup Of Machine Learning Forecasts And Market Estimates, 2020 for additional market research on A.I. and machine learning.

The following graphic compares the top 10 most popular locations for A.I. & ML startups globally based on Crunchbase data as of today:

Top 25 Machine Learning Startups To Watch In 2021

Augury – Augury combines real-time monitoring data from production machinery with AI and machine learning algorithms to determine machine health, asset performance management (APM) and predictive maintenance (PdM) to provide manufacturing companies with new insights into their operations. The digital machine health technology that the company offers can listen to the machine, analyze the data and catch any malfunctions before they arise. This enables customers to adjust their maintenance and manufacturing processes based on actual machine conditions. The platform is in use with HVAC, industrial factories and commercial facilities.

Alation – Alation is credited with pioneering the data catalog market and is well-respected in the financial services community for its use of A.I. to interpret and present data for analysis. Alation has also set a quick pace to evolving its platform to include data search & discovery, data governance, data stewardship, analytics and digital transformation. With its Behavioral Analysis Engine, inbuilt collaboration capabilities and open interfaces, Alation combines machine learning with human insight to successfully tackle data and metadata management challenges. More than 200 enterprises are using Alation’s platform today, including AbbVie, American Family Insurance, Cisco, Exelon, Finnair, Munich Re, New Balance, Pfizer, Scandinavian Airlines and U.S. Foods. Headquartered in Silicon Valley, Alation is backed by leading venture capitalists including Costanoa, Data Collective, Icon, Sapphire and Salesforce Ventures.

Algorithmia – Algorithmia’s expertise is in machine learning operations (MLOps) and helping customers deliver ML models to production with enterprise-grade security and governance. Algorithmia automates ML deployment, provides tooling flexibility, enables collaboration between operations and development and leverages existing SDLC and CI/CD practices. Over 110,000 engineers and data scientists have used Algorithmia’s platform to date, including the United Nations, government intelligence agencies and Fortune 500 companies.

Avora – Avora is noteworthy for its augmented analytics platform, making in-depth data analysis intuitively as easy as performing web searches. The company’s unique technology hides complexity, empowering non-technical users to run and share their reports easily. By eliminating the limitations of existing analytics, reducing data preparation and discovery time by 50-80% and accelerating time to insight, Avora uses ML to streamline business decision-making. Headquartered in London with offices in New York and Romania, Avora helps accelerate decision making and productivity for customers across various industries and markets, including Retail, Financial Services, Advertising, Supply Chain and Media and Entertainment.

Boast.ai – Focused on helping companies in the U.S. and Canada recover their R&D costs from respective federal governments, Boast.ai enables engineers and accountants to gain tax credits using AI-based tools. Some of the tax programs Boast.ai works with include US R&D Tax Credits, Scientific Research and Experimental Development (SR&ED) and Interactive Digital Media Tax Credits (IDMTC). The startup has offices in San Francisco, Vancouver and Calgary.

ClosedLoop.ai – An Austin, Texas-based startup, ClosedLoop.ai has created one of the healthcare industry’s first data science platforms that streamline patient experiences while improving healthcare providers’ profitability.  Their machine learning automation platform and a catalog of pre-built predictive and prescriptive models can be customized and extended based on a healthcare provider’s unique population or client base needs. Examples of their technology applications include predicting admissions/readmissions, predicting total utilization & total risk, reducing out-of-network utilization, avoiding appointment no-shows, predicting chronic disease onset or progression and improving clinical documentation and reimbursement. The Harvard Business School, through its Kraft Precision Medicine Accelerator, recently named ClosedLoop.ai as one of the fastest accelerating companies in it’s Real World Data Analytics Landscapes Report.

Cognino AI – Cognino AI is a London-based startup specializing in research-led A.I. with deep expertise in self-learning Explainable A.I. They’re noteworthy for helping their clients accelerate data preparation to insight from large sets of unstructured data to support strategic decisions through real learning A.I.

Databand – A Tel Aviv-based startup that provides a software platform for agile machine learning development, Databand was founded in 2018 by Evgeny Shulman, Joshua Benamram and Victor Shafran. Data engineering teams are responsible for managing a wide suite of powerful tools but lack the utilities they need to ensure their ops are running properly. Databand fills this gap with a solution that enables teams to gain a global view of their data flows, make sure pipelines complete successfully and monitor resource consumption and costs. Databand fits natively in the modern data stack, plugging seamlessly into tools like Apache Airflow, Spark, Kubernetes and various ML offerings from the major cloud providers.

DataVisor – DataVisor’s approach to using AI for increasing fraud detection accuracy on a platform level is noteworthy. Using proprietary unsupervised machine learning algorithms, DataVisor enables organizations to detect and act on fast-evolving fraud patterns and prevent future attacks before they happen. Combining advanced analytics and an intelligence network of more than 4.2B global user accounts, DataVisor protects against financial and reputational damage across various industries, including financial services, marketplaces, e-commerce and social platforms. They’re one of the more fascinating cybersecurity startups using AI today.

Exceed.ai – What makes Exceed.ai noteworthy is how their AI-powered sales assistant platform automatically communicates the context of leads and enables sales and marketing teams to scale their lead engagement and qualification efforts accordingly. Exceed.ai follows up with every lead and qualifies them quickly through two-way, automated conversations with prospects using natural language over chat and email. Sales reps are freed from performing error-prone and repetitive tasks, allowing them to focus on revenue-generating activities such as phone calls and demos with potential customers.

Indico – Indico is a Boston-based startup specializing in solving the formidable challenge of how dependent businesses are on unstructured content yet lack the frameworks, systems and tools to manage it effectively. Indico provides an enterprise-ready A.I. platform that organizes unstructured content while streamlining and automating back-office tasks. Indico is noteworthy given its track record of helping organizations automate manual, labor-intensive, document-based workflows. Its breakthrough in solving these challenges is an approach known as transfer learning, which allows users to train machine learning models with orders of magnitude fewer data than required by traditional rule-based techniques. Indico enables enterprises to deploy A.I. to unstructured content challenges more effectively while eliminating many common barriers to A.I. & ML adoption.

JAXJOX – JAXJOX created InteractiveStudio, a fitness platform that combines connected strength-training equipment with live and on-demand content. JAXJOX creates compact, smart workout products with AI-powered performance tracking and a variety of live and on-demand classes for educational motivation, making it easy for its subscribers to reach wellness goals from the comfort of their home. The company was founded in 2016 and is based in Bellevue, Washington. JAXJOX has raised a total of $17 million in funding over two rounds.

LeadGenius – LeadGenius is noteworthy for its use of AI to provide personalized and actionable B2B lead information that helps its clients attain their global revenue growth goals. LeadGenius’s worldwide team of researchers uses proprietary technologies, including AI and ML-based techniques, to deliver customized lead generation, lead enrichment and data hygiene services in the format, methods and frequency defined by the customer. Their mission is to enable B2B sales and marketing organizations to connect with their prospects via unique and personalized data sets.

Netra – Netra is a Boston-based startup that began as part of MIT CSAIL research and has multiple issued and pending patents on its technology today. Netra is noteworthy for how advanced its video imagery scanning and text metadata interpretation are, ensuring safety and contextual awareness. Netra’s patented A.I. technology analyzes videos in real-time for contextual references to unsafe content, including deepfakes and potential cybersecurity threats.  

Particle –  Particle is an end-to-end IoT platform that combines software including A.I., hardware and connectivity to provide a wide range of organizations, from startups to enterprises, with the framework they need to launch IoT systems and networks successfully. Particle customers include Jacuzzi, Continental Tires, Watsco, Shifted Energy, Anderson EV, Opti and others. Particle is venture-backed and has offices in San Francisco, Shenzhen, Las Vegas, Minneapolis and Boston. Particle’s developer community includes over 200,000 developers and engineers in more than 170 countries today.

Resurface Labs – Resurface provides a user-centric view into APIs that helps to democratize big data insights. Using AI-based techniques combined with their unique analysis techniques, Resurface turns every API call into a durable transaction to speed troubleshooting, drive revenue recovery and improve CX. Resurface is gaining adoption in pre-production testing and Q.A., DevOps troubleshooting and root cause analysis and real user data for data science spelunking across DevOps organizations today.

RideVision – RideVision was founded in 2018 by motorcycle enthusiasts Uri Lavi and Lior Cohen. The company is revolutionizing the motorcycle-safety industry by harnessing the strength of artificial intelligence and image-recognition technology, ultimately providing riders with a much broader awareness of their surroundings, preventing collisions and enabling bikers to ride with full confidence that they are safe. RideVision’s latest round was $7 million in November of last year, bringing their total funding to $10 million in addition to a partnership with Continental AG.

Savvie – Savvie is an Oslo-based startup specializing in translating large volumes of data into concrete actions that bakery and café owners can utilize to improve their bottom line every day. In doing so, we help food businesses make the right decisions to optimize their operations and increase profitability while reducing waste at its source. What’s noteworthy about this startup is how adept they are at fine-tuning ML algorithms to provide their clients with customized recommendations and real-time insights about their food and catering businesses.  Their ML-driven insights are especially valuable given how bakery and café owners are pivoting their business models in response to the pandemic.

SECURITI.ai – One of the most innovative startups in cybersecurity, combining AI and ML to secure sensitive data in multi-cloud and mixed platform environments, SECURITI.ai is a machine learning company to watch in 2021, especially if you are interested in cybersecurity.  Their AI-powered platform and systems enable organizations to discover potential breach risk areas across multi-cloud, SaaS and on-premise environments, protect it and automate all private systems, networks and infrastructure functions.

SkyHive – SkyHive is an artificial intelligence-based SaaS platform that aims to reskill enterprise workforces and communities. It develops and commercializes a methodology, Quantum Labor Analysis, to deliver real-time, skill-level insights into internal workforces and external labor markets, identify future and emerging skills and facilitate individual-and company-level reskilling. SkyHive is industry-agnostic and supporting enterprise and government customers globally with a mission to reduce unemployment and underemployment. Sean Hinton founded the technology company in Vancouver, British Columbia, in 2017.

Stravito – Stravito is an A.I. startup that’s combining machine learning, Natural Language Processing (NLP) and Search to help organizations find and get more value out of the many market research reports, competitive, industry, market share, financial analysis and market projection analyses they have by making them searchable. Thor Olof Philogène and Sarah Lee founded the company in 2017, who identified an opportunity to help companies be more productive, getting greater value from their market research investments. Thor Olof Philogène and Andreas Lee were co-founders of NORM, a research agency where both worked for 15 years serving multinational brands, eventually selling the company to IPSOS. While at NORM, Anders and Andreas were receiving repeated calls from global clients that had bought research from them but could not find it internally and ended up calling them asking for a copy. Today the startup has Carlsberg, Comcast, Colruyt Group, Danone, Electrolux, Pepsi Lipton and others. Stravito has offices in Stockholm (H.Q.), Malmö and Amsterdam.

Uniphore – Noteworthy for its expertise in Conversational Service Automation (CSA), which combines the power of artificial intelligence, automation technology and machine learning, Uniphore shows potential to transform customer service.  Conversational Service Automation’s value proposition is predicated on giving customer service agents improved agent conversation quality, automating agent tasks, automatic disposition capture. Their AI-based platform includes Speech Analytics, Virtual Assistant and Voice Biometrics, gaining momentum in their enterprise accounts.

Vertia.ai – Verta is a startup dedicated to solving the complex problems of managing machine learning model versions and providing a platform where they can be launched into production. Founded by Dr. Manasi Vartak, Ph.D., a graduate of MIT, who led a team of graduate and undergraduate students at MIT CSAIL to build ModelDB, Verta is based on their work define the first open-source system for managing machine learning models. Her dissertation, Infrastructure for model management and model diagnosis, proposes ModelDB, a system to track ML-based workflows’ provenance and performance. In August of this year, Verta received a $10 million Series A round led by Intel Capital and General Catalyst, who also led its $1.7 million seed round. For additional details on Verta.ai, please see How Startup Verta Helps Enterprises Get Machine Learning Right.

V7 – V7 allows vision-based A.I. systems to learn continuously from training data with minimal human supervision. The London-based startup emerged out of stealth in August 2018 to reveal V7 Darwin, an image labeling platform to create training data for computer vision projects with little or no human involvement necessary. V7 specializes in healthcare, life sciences, manufacturing, autonomous driving, agri-tech, sporting clients like Merck, GE Healthcare and Toyota. V7 Darwin launched at CVPR 2019 in Long Beach, CA. Within its first year, it has semi-automatically annotated over 1,000 image and video segmentation datasets. V7 Neurons is a series of pre-trained image recognition applications for industry use. The following video explains how V7 Darwin works:

 Zest.ai – Zest.ai’s mission is to provide its partners in highly regulated financial lending industries with powerful, compliant machine learning models swiftly, so they’re able to identify dependable borrowers in their respective businesses. Zest.ai is noteworthy for its deep expertise in financial lending and its creation the Zest Automated Machine Learning (ZAML®). ZAML helps Zest.ai’s partners accurately identify more dependable borrowers efficiently, resulting in increased revenue, reduced risk and automated compliance. Zest.ai’s mission is to make fair and transparent credit available to everyone. Zest.ai’s content, from blog posts to white papers and ebooks, is among the best of AI-based startups in fintech. One of the best articles this year on truth-testing A.I. was written by Zest.ai’s CTO Jay Budzik titled, How You Can Tell If An A.I. Startup Is Bogus.

Published at Mon, 11 Jan 2021 04:30:00 +0000

The future of EdTech: How the global education system is in for a complete makeover

COVID-19 has been able to accomplish what armies of dissatisfied parents, charismatic speakers like Ken Robinson and Sugata Mitra, and piles of research on education policy could not do — give ‘innovation’ a chance.

We are currently in the midst of a worldwide experiment led by the digital and ideological transformation. While digital technologies have been in place for many decades, change had been slow to come in K-12 education but COVID -19 precipitated this shift by affecting 1.6 billion students across the world at one go; all stakeholders were forced to adapt at once- children, students, schools, parents and policymakers.

This transformation was further bolstered by the New Education Policy 2020 which insists that robotics, programming, artificial intelligence and machine learning be included in the existing school curriculum to help children develop crucial 21st-century skills such as scientific temper, computational thinking, logical reasoning and evidence-based thinking.

With the ongoing pace of the changes made in the sector, it will not be far-fetched to say that by the time the crisis is behind us, our education system globally would be affected in deep enough ways that it will not be able to revert to the previous ways of viewing education. 

Behavioral Change and Technology 

The pandemic has changed the way we view education and the role of technology in it. The subsequent shift in the medium of learning in 2020, from on-ground to almost entirely online and hence its acceptance on the part of students, parents and educators has shown a behavioural and technological change in how we approach education. Students have been at the helm of technological change since a few years, relying increasingly on internet-based platforms such as Quora or Youtube instead of textbooks for knowledge or problem-solving. This reliance on technology to learn, since the outbreak of the pandemic, has been mirrored by the teachers and schools alike, through uploading classes on Youtube or taking a live classroom session on Zoom. Hence, a heavier reliance on technology is a trend most likely to be seen in 2021.

Flipped Learning will get a chance

2021 is definitely set out to give flipped learning a chance, if not entirely inculcate it. Flipped learning is a pedagogical approach, a type of a blended learning model in which the traditional way of teaching–one that follows a lecture-style in which the teacher is the primary disseminator of information changes, to a more dynamic, learner-centred approach in which students engage the topics in greater depth in the classroom while they are introduced to new concepts outside of the classroom–via video lessons or other means. 

Tie in with the Gig Economy 

The behavioural shift of the young minds is not limited to education but also to the way they view life. Gen Z, today, have aspirations set deep down to the kind of work they want to do and consequently their work-life balance. This transformation has led to the formation of the ‘gig’ economy, a system driven by contact assignments where temporary roles are the norm. Ed-tech platforms come in handy for this cohort through the skills they provide in order to thrive in the gig economy. Hence, one trend 2021 will likely bring from the close link between the Ed-tech sector and the gig economy, is increasing focus on the skills catering to the gig economy aspirants. 

Nature of assessment will evolve

The current education system has been limited in its own sense for decades and has therefore had a snowballing effect in terms of future employability and nurturing an innovative mindset. 
The healthcare crisis has propelled industries to drive innovation, primarily technological, in order to survive. COVID-19 has been about survival, thus changing the nature of our assessment to a more evolved approach. In order to stay afloat and relevant in the contemporary world, creativity and innovativeness, collaboration and teamwork, problem-solving, logical reasoning, vocational exposure, digital literacy and coding are at the heart of the new ways to assess workforce and businesses.

Hands-on learning

The hands-on learning model is one in which kids learn new concepts by ‘doing’ or application. Instead of merely applying their listening faculty, student engagement lies at the crux of the hands-on learning approach. STEAM fields to a large extent are based upon constructionist learning wherein students learn-by-making. 2020 has been an instrumental year towards following this approach of learning, as is shown by the influx of students pursuing their interest in such fields. 2021, by all means, is ready and likely to embrace this change with open arms.
 

Pooja Goyal is the Co-Founder and COO of Avishkaar.

Published at Mon, 11 Jan 2021 04:30:00 +0000

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