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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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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.

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

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.

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.

Artificial Intelligence, Big Data and Fundamental Rights AI policy initiatives

A ‘policy initiative’ was defined broadly to include a range of initiatives that could potentially contribute to policy making and standard setting in the area of AI.

This could be, amongst others, actual (draft) legislation, soft-law, guidelines and recommendations on the use of AI, or reports that include conclusions with policy relevance.

In other cases, the initiatives come from data protection authorities, other state institutions, academia, NGOs, an EU body or international organisations.

Big Data, Artificial Intelligence, and Ethics

This course gives you context and first-hand experience with the two major catalyzers of the computational science revolution: big data and artificial intelligence.

As hands-on labs, you will use IBM Watson’s artificial intelligence to extract the personality of people from their digital text traces, and you will experience the power and limitations of machine learning by teaching two teachable machines from Google yourself.

Big Data, Artificial Intelligence and Business Intelligence – what’s the common currency?

Raw source data tends to be reshaped in different ways to serve different functions, however, resulting in the starting points for conversations often using multiple base definitions.

So, whilst data is a common asset used across business functions there is often not a common currency to bind strategic conversations.

In a typical business, long lasting success is achieved by affordably delivering a brand value proposition which attracts profitable customers who remain loyal and ideally advocate it to others.

This all needs robust planning, measurement and ongoing refinement to ensure the people working in the business adapt their behaviours and outcomes successfully to an ever-changing business landscape.

Building the appropriate tech stack can be complex, but the single biggest challenge in large organisations is to get everyone working towards the same outcomes in an effective way.

This common currency which binds conversations between strategy, value proposition, AI, activity plans (marketing and operations), HR and BI however is simple – it’s people!

   To de-risk the future, and secure long-lasting success, it is critical for businesses, particularly large organisations, to implement People Intelligence (PI) in all corners of their operation.

That old adage ‘the customer is always right’ cannot exist without the necessary opposite ‘the employee is always wrong’ – and that’s no way to run a company.

Artificial Intelligence (AI) in Big Data, Data as a Service (DaaS), AI Supported IoT (AIoT) and AIoT DaaS (2019-2024) - ResearchAndMarkets.com

AIoT market elements will be found embedded within software programs, chipsets, and platforms as well as human-facing devices such as appliances, which may rely upon a combination of local and cloud-based intelligence.

Just like the human nervous system, IoT networks will have both autonomic and cognitive functional components that provide intelligent control as well as nerve end-points that act like nerve endings for neural transport (detection and triggering of communications) and nerve channels that connect the overall system.

It is destined to become an integral component of business operations including supply chains, sales and marketing processes, product and service delivery and support models.

The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with edge computing networks.

The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic.

The convergence of AI and IoT technologies and solutions (AIoT) is leading to thinking networks and systems that are becoming increasingly more capable of solving a wide range of problems across a diverse number of industry verticals.

These solutions will focus primarily upon optimizing system and network operations as well as extracting value from industry data through dramatically improved analytics and decision-making processes.

AIoT infrastructure and services will therefore be leveraged to achieve more efficient IoT operations, improve human-machine interactions and enhance data management and analytics, creating a foundation for IoT Data as a Service (IoTDaaS) and AI based Decisions as a Service.

Looking beyond data management processes, IoT data itself will become extremely valuable as an agent of change for product development as well as identification of supply gaps and realization of unmet demands.

Data generated through sensors embedded in various things/objects will generate massive amounts of unstructured (big) data on real-time basis that holds the promise for intelligence and insights for dramatically improved decision processes.

Big data in IoT is also dissimilar than non-machine related analytics and thus will require more robust, agile and scalable platforms, analytics tools, and data storage systems than conventional infrastructure.

The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with edge computing networks.

The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with edge computing networks.

However, real-time data is anticipated to become a highly valuable aspect of all solutions as a determinant of user behavior, application effectiveness, and identifier of new and enhanced mobile/wireless and/or IoT related apps and services.

We see the AIoT market transforming from today's largely consumer appliance and electronics related approach to one in which AIoT data is highly valued asset wherein companies like SAS provide a utility function in terms of helping enterprise, industrial, and government clients monetize their data.

As it is prohibitively difficult to identify all of the sources and source types, we have broadly segmented Source by Machine Data (consumer appliances, vehicles [ cars, trucks, planes, trains, ships, etc.

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