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32 artificial intelligence companies building a smarter tomorrow

From Google and Amazon to Apple and Microsoft, every major tech company is dedicating resources to breakthroughs in artificial intelligence.

Meanwhile, revolutionary breakthroughs like self-driving cars may not be the norm, but are certainly within reach.  As the big guys scramble to infuse their products with artificial intelligence, other companies are hard at work developing their own intelligent technology and services.

By highlighting only the most relevant and interesting information, businesses can make quicker decisions regardless of the staff's experience with data or analytics.

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Using non-invasive blood tests, the company’s AI technology recognizes disease-associated patterns, providing earlier cancer detection and better treatment options.

Industry: Fintech Location: New York, New York What it does: AlphaSense is an AI-powered search engine designed to help investment firms, banks and Fortune 500 companies find important information within transcripts, filings, news and research. The technology uses artificial intelligence to expand keyword searches for relevant content.

Its LiDAR technology focuses on the most important information in a vehicle’s sightline such as people, other cars and animals, while putting less emphasis on things like the sky, buildings and surrounding vegetation.

By fusing problem solving, learning and memory technologies together, the company can build systems that learn and adapt without human assistance.

Called CARA A.I., the company's tech can search within the language, jurisdiction and citations of a user's uploaded documents and return relevant searches from the database.

Industry: Cloud, Robotics Location: Santa Clara, California What it does: CloudMinds provides cloud robot services for the finance, healthcare, manufacturing, power utilities, public sector and enterprise mobility industries.

Its cloud-based AI uses advanced algorithms, large-scale neural networks and training data to make smarter robots for image and object recognition, natural language processing, speech recognition and more.

The company's 'human-in-the-loop' platform uses human intelligence to train and test machine learning, and has powered AI projects for major companies like Oracle, Ebay SAP and Adobe.

From financial and insurance needs to travel and healthcare, the intelligent products perform duties and answer questions for tech support, billing, scheduling, purchases and policy information.

Industry: Big Data, Software Location: Mountain View, California What it does: Orbital Insight uses geospatial imagery and artificial intelligence to answer questions and gain insights invisible to the naked eye. Using data from satellites, drones, balloons and other aircrafts, the company can provide insights and forecasts to the agriculture and energy industries that normally wouldn’t be available.

Industry: Software Location: San Francisco, California What it does: OpenAI is a nonprofit research company with a mission to create safe artificial general intelligence (AGI). AGI aims to create machines with general purpose intelligence similar to human beings. With a focus on long-term research and transparency, OpenAI hopes to advance AGI safely and responsibly.

Sift uses thousands of data points from around the web to train in detecting fraud patterns. The technology helps payment processors, marketplaces, e-commerce stores and even social networks prevent fraud.

The company, which boasts a mission to eventually create machines that surpass human intelligence, has serious backing from tech titans like Mark Zuckerberg, Jeff Bezos and Elon Musk.

Industry: Software, Healthtech Location: Berkeley, California (US office) What it does: Zebra Medical Vision develops technology for radiology and medical imaging, enhancing the diagnostic abilities of radiologists while maximizing focus on patient care.

These algorithms will ultimately help medical professionals detect high-risk patients earlier and manage growing workloads with more accurate outcomes.

Spanning the agriculture, pharmaceutical and chemical industry, the company enables faster cultivation of microbes through automation software and a huge catalog of physical and digital DNA data.

AI in Healthcare — help or hindrance?

By Luke Kenworthy on April 26, 2019 There can be no question that Artificial Intelligence (AI) is set to alter the face of various industries, changing the landscape of both labour and skill based employment tasks.

Initial industry literature recognises AI as a viable and realistic treatment for a number of ailments, including, but not limited to eye disease, sepsis, skin cancer, heart disease, lung cancer, Parkinson’s, strokes, despite current application examples being small-scale pilots or research projects.

Recent studies have suggested that the aforementioned collaborative effort could be increasingly important for industry progression, with trials suggesting that in image classification alone, localisation scores and diagnosis increased significantly with not only increased efficiency but a decrease of 85% in human error rate with the aid of intelligent agents.

There is no doubt that in times gone by, a local family doctor could be seen as the sole avenue for medical advice, however, in recent years there has been an increasingly open-minded approach to all things tech in regard to patient care, with 75% of U.S. consumers surveyed citing that AI technological advances (including mobile apps, wearable monitoring devices, and smart scales) were important to them to help them manage their health and an even larger amount being happy to rely on AI-based systems for symptom diagnosis.

Artificial Intelligence Boosts Healthcare Advancements

Although digital and technological transformation has been at a slow pace in the medical space, the industry soon matched up to the changing market dynamics.    In recent time, one technology that has been making news across industries is artificial intelligence (AI).

With recent developments and achievements pertaining to AI in healthcare, the work of healthcare providers has become more streamlined and logical.   Here are some of the areas in healthcare where AI is supporting providers with early diagnosis, faster service, and data analysis.

In a study that involved 379 orthopedic patients, it was observed that AI-assisted robotic procedure performed way better than the procedure where only the surgeon worked alone.

However, the penetration of AI in nursing is in the evolution stage with various research and experimentation works being undertaken.   From being 24/7 available for help to quickly answering patient queries, performing wellness checks and education patients, virtual assistants are transforming the way care delivered.

Especially in the medical sector, although implementations may not be noticeable yet, various research and case studies are underway focusing on the application of AI in healthcare operations.

With analytical reasoning capabilities, the assistant will reduce the workload of clinicians helping them to get a better view of the test image for an accurate diagnosis.

As a result, healthcare providers fail to recognize the potentiality of automation and how it can help them provide better patient experience and improve care quality at minimal costs.

As per reports, automation alone is expected to save $18 billion for the healthcare industry which can help reduce the overall healthcare cost and expenses.

Using a voice-to-text transcription tool, providers can perform administration related works like prescribing medicine, revenue cycle management, scheduling appointments, maintaining medical records, managing patient history, billing, ordering tests, reporting, etc.

However, with the advent of artificial intelligence and machine learning into the field, image analysis is up to 1,000 times faster than the manual method.

The patients can just use their phone camera and send pictures of cuts, rashes or bruises to the doctor who can get it quickly analyzed with AI-driven tools or software and suggest immediate care.

Artificial Intelligence Industry – An Overview by Segment

This article serves as a living overview of existing efforts by media, research firms, and others, who have attempted to move from the eagle eye’s view of the AI industry to categorizing technologies under the grand umbrella. Older breakdowns and analyses begin at top of the article and move down to newer trends.

Numbers in parentheses are in millions: It is interesting to note that BCC predicts the highest aggregate 5-year growth rate in the area of digital assistants, which seems to corroborate with our own 5-year AI trends executive consensus.

The full breakdown of categories above are listed below: Agents Autonomous Systems Enterprise Platforms Industries Tech User Tools The two biggest changes I’ve noted since I did this analysis last year are (1) the emergence of autonomous systems in both the physical and virtual world and (2) startups shifting away from building broad technology platforms to focusing on solving specific business problems.

Key differences noted in Zillis’  Machine Intelligence 3.0 version are more pronounced and refined, as technologies and the industry continue to advance: The full breakdown of categories above are listed below: Enterprise Intelligence Enterprise Functions Autonomous Systems Agents Industries Healthcare Technology Stack For the first time, a “one stop shop” of the machine intelligence stack is coming into view—even if it’s a year or two off from being neatly formalized.

CB Insights is a New York-based research firm with under 200 employees, specializing in tech intelligence from various sources, to include venture capital, startups, patents, partnerships, and new media.

companies focus their efforts, as those domains will almost invariably gather more momentum and excitement as this nascent field looks to new leaders vying to become global AI platforms / solutions for direction and focus.

Graphics processing unit (GPU) is a specialized electronic circuit, designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display.

While GPU usage is by no means a causal influence on the applications of AI in a given industry, it provides a certain amount of context on industry growth (Nvidia claims to have sold GPUs to nearly 100 times more companies in 2015 than in 2013, a significant leap).

While this graphic does draw attention to various discrete industries and companies, it seems to be a bit more broadly focused and includes a number of startup but also a handful of companies like Yaskawa (founded over 100 years ago), Apple (Siri) and Nvidia, which don’t seem to belong in a graphic labeled as “startups.”

The following AI Sector Map breaks down into 13 broad categories, with the total number of active companies in that sector in parentheses: The results from VentureScanner’s March 2016 report are as follows: Other charts on the VentureScanner report page show funding by AI category (by far most are in machine learning (ML) apps, followed by NLP);

average age of technology by AI category (speech to speech translation oldest, followed by gesture control, video content recommenders, and speech recognition); among others.

The vast majority of companies interviewed had nothing to do with chat bots or personal assistants, yet over a third of all executive responses expressed confidence in chat bots as the most influential consumer AI technology in the coming give years.

It’s important to note that the question of technology trends was presented in an open-ended fashion, and categories (such as “smart objects / environment”, “virtual agents”, etc…) were applied after analyzing individual responses.

Below are a few worth considering: Markets and Markets breaks out AI verticals into the following main categories in their 2020 AI Forecast: Tractica’s recent AI Applications in Enterprise report breaks down technology forecasts as follows: The amount of reliable information about the AI market is less than ideal, and far less quantified than more mature and established markets.

AI companies who begin by working on Wall Street may be perceived as simply profit-driven, or possibly helping the wrong party, while a company devoting itself to curing disease or improving treatment (even if for the exact same profit motive) may be viewed in a different light.

I’m of the belief that the convoluted sales cycles and market forces in health care will lead to finance, eCommerce, and marketing leaping ahead in terms of relative AI adoption and innovation, though only the future will tell.

breakdown as a good general breakdown, and BCC has a nice simple segmentation as well), but that fuzzy edges will always be present in this kind of work, and that a consensus across research firms is unlikely (indeed, a consensus within any one research firm seems unlikely).

resulted in a number of observable behaviors and patterns in the AI industry: Despite the disagreements about specific industry or domain application and adoption, the rumble on the tracks (from all directions) seems to liken AI not to a specific tool for a few specific jobs, but as an entirely different (and in large part unimaginable) paradigm of work, research, and productivity.

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