AI News, 32 Artificial Intelligence Companies You Should Know
Leveraging Machine Learning and Artificial Intelligence for 5G
The heterogenous nature of future wireless networks comprising of multiple access networks, frequency bands and cells - all with overlapping coverage areas - presents wireless operators with network planning and deployment challenges.
ML and AI can assist in finding the best beam by considering the instantaneous values updated at each UE measurement of the parameters mentioned below: Once the UE identifies the best beam, it can start the random-access procedure to connect to the beam using timing and angular information.
Massive simply refers to the large number of antennas (32 or more logical antenna ports) in the base station antenna array. Massive MIMO enhances user experience by significantly increasing throughput, network capacity and coverage while reducing interference by: The weights for antenna elements for a massive MIMO 5G cell site are critical for maximizing the beamforming effect.
ML and AI can collect real time information for multidimensional analysis and construct a panoramic data map of each network slice based on: Different aspects where ML and AI can be leveraged include: With future heterogenous wireless networks implemented with varied technologies addressing different use cases providing connectivity to millions of users simultaneously requiring customization per slice and per service, involving large amounts of KPIs to maintain, ML and AI will be an essential and required methodology to be adopted by wireless operators in near future.
All of them address low latency use cases where the sensing and processing of data is time sensitive. These use cases include self-driving autonomous vehicles, time-critical industry automation and remote healthcare. 5G offers ultra-reliable low latency which is 10 times faster than 4G. However, to achieve even lower latencies, to enable event-driven analysis, real-time processing and decision making, there is a need for a paradigm shift from the current centralized and virtualized cloud-based AI towards a distributed AI architecture where the decision-making intelligence is closer to the edge of 5G networks.
The 5G mm-wave small cells require deep dense fiber networks and the cable industry is ideally placed to backhaul these small cells because of its already laid out fiber infrastructure which penetrates deep into the access network close to the end-user premises.
People in the Nordics are worried about the development of AI – personal data processing a major concern
Despite the many advantages artificial intelligence (AI) bring, Nordic consumers are concerned about how the technology is used today. More than eight out of ten (85 per cent) say that they are worried about the development of AI and only six per cent state that they are not worried at all. At the same time, an overwhelming majority (80 per cent) state that development should be regulated to some extent. This is revealed in a survey by Tieto that was carried out with help of the research firm YouGov.
The biggest concern about letting AI handle personal data is linked to a fear of it being misused (55 per centstate this),used for wrong and immoral purposes (46 per cent),usedin an unsafe way(49 per cent),orthat misinterpretations can occur (53 per cent).
“We know that it takes time to build confidence in new technologies and especially AI are today associated with somethingunfamiliar andnegative.It isthereforeessential to highlight the positiveaspects ofAI and the many benefits it enables, from speeding up health diagnoses to creating better everyday experiences.As a company, we also recognize that trust mustbe earned and that we must createAI solutionsthat are ethicalandresponsibly developedwith a high degree of transparency andsecurity.Trust in technology is essential for our society to fully benefit from AI and data-driven solutions, and we are the Nordic company that has solid experience in this emerging enterprise industry”, says Christian Guttmann.
This must be donein an ethicalandresponsibleway and itis important thatwe work together within theindustryandacross borders,todevelopstandards andguidelines.Several initiatives are already underway, including the EU,which will be valuableinthe continued development of AI and,in the long term, can contribute tocreating broader trust amongst the public”, says Christian Guttmann.
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.
Cerebras Systems Unveils 1.2 Trillion Transistor Wafer-Scale Processor for AI
The Cerebras WSE contains 400,000 sparse linear algebra cores, 18GB of total on-die memory, 9PB/sec worth of memory bandwidth across the chip, and separate fabric bandwidth of up to 100Pbit/sec.
Because the chip is built from (most) of a single wafer, the company has implemented methods of routing around bad cores on-die and can keep its arrays connected even if it has bad cores in a section of the wafer.
is cooled using a massive cold plate sitting above the silicon, with vertically mounted water pipes used for direct cooling.
fully functional wafer-scale processor, commercialized at scale, would be an exciting demonstration of whether this technological approach has any relevance to the wider market.
While we’re never going to see consumer components sold this way, there’s been interest in using wafer-scale processing to improve performance and power consumption in a range of markets.
If consumers continue to move workloads to the cloud, especially high-performance workloads like gaming, it’s not crazy to think we might one day see GPU manufacturers taking advantage of this idea —
- On 15. april 2021
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