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The 30 top artificial intelligence companies most well founded

#artificialintelligence

Artificial intelligence (AI) is currently one of the most disruptive technologies, and it is a great means for startups to achieve their hyper-growth goals. Artificial intelligence has numerous applications in fields such as big data, computer vision, and natural language processing, and is revolutionizing businesses, industries, and people's lives. Among the most well-funded and promising independent startups, the majority of the top Artificial Intelligence companies are from the US or China, with many more countries participating. The benefits of AI in many industries are evident in these two key countries, but each country seems to have slightly different concerns. The largest AI startups in the U.S. are particularly present in the areas of big data analytics and process automation for business, autonomous driving and biotechnology.


Business intelligence firm Pyramid Analytics raises $120M – TechCrunch

#artificialintelligence

Business intelligence is an increasingly well-funded category in the software-as-a-service market. By handling large amounts of data to analyze and benchmark lines of business, BI promises to help identify, develop and otherwise create new revenue opportunities. Pervasive BI remains elusive, but statistics on the category reveal that about a third of employees use BI tools for analytics to inform strategy. The big data and business analytics market could be worth $684 billion by 2030, according to Valuates Reports, if such outrageously high estimates are to be believed. The segment contains too many vendors to count -- a few include Noogata, Fractal Analytics, Tredence, LatentView and Mu Sigma.


10 startups riding the wave of AI innovation

#artificialintelligence

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Organizations are increasingly adopting AI-enabled technologies to address existing and emerging problems within the enterprise ecosystem, meet changing market demands and deliver business outcomes at scale. Shubhangi Vashisth, senior principal research analyst at Gartner, said that AI innovation is happening at a rapid pace. Vashisth further noted that innovations including edge AI, computer vision, decision intelligence and machine learning will have a transformational impact on the market in coming years. However, while AI-powered technologies are helping to build more agile and effective enterprise systems, they usher in new challenges. For example, Gartner notes that AI-based approaches if left unchecked can perpetuate bias, leading to issues, loss of productivity and revenue.


Government Deep Tech 2022 Top Funding Focus Explainable AI, Photonics, Quantum

#artificialintelligence

DARPA, In-Q-Tel, US National Laboratories (examples: Argonne, Oak Ridge) are famous government funding agencies for deep tech on the forward boundaries, the near impossible, that have globally transformative solutions. The Internet is a prime example where more than 70% of the 7.8 billion population are online in 2022, closing in on 7 hours daily mobile usage, and global wealth of $500 Trillion is powered by the Internet. There is convergence between the early bets led by government funding agencies and the largest corporations and their investments. An example is from 2015, where I was invited to help the top 100 CEOs, representing nearly $100 Trillion in assets under management, to look ten years into the future for their investments. The resulting working groups, and private summits resulted in the member companies investing in all the areas identified: quantum computing, block chain, cybersecurity, big data, privacy and data, AI/ML, future in fintech, financial inclusion, ...


La veille de la cybersécurité

#artificialintelligence

Banking and fintech firms have been using artificial intelligence (AI) for the last few years to improve fraud detection on credit and debit cards, analyze patterns of defaulters, caution users from overspending and even help them determine their spendings. Some companies have now also begun using predictive analytics to enhance how credit and debit cards are being used in real time. For instance, Philadelphia-based fintech firm cred.ai, The card was licenced by payments network Visa and issued by Wilmington Savings Fund Society, FSB. The credit optimizer tool uses an AI algorithm to improve the user's debt-to-credit ratio, which accounts for up to 30% of a FICO score that evaluates a person's creditworthiness in the US.


AI-based advanced analytics is making credit, debit cards smarter

#artificialintelligence

For instance, Philadelphia-based fintech firm cred.ai, The card was licenced by payments network Visa and issued by Wilmington Savings Fund Society, FSB. The credit optimizer tool uses an AI algorithm to improve the user's debt-to-credit ratio, which accounts for up to 30% of a FICO score that evaluates a person's creditworthiness in the US. Apple, too, uses AI to determine a user's credit limit on the Apple Card. Closer home, Gurugram-based fintech firm OneBanc has developed a card to connect various banking systems.


Artificial Intelligence Trends That Will Dominate In 2022

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In 2022, businesses will be using artificial intelligence (AI) in more innovative ways than ever before. Some of the most popular ways that AI will be used in businesses include: Chatbots will be used to communicate with customers. AI will be used to analyze data and make decisions. Robotics will be used to automate tasks. AI will be used to create new products and services. Virtual assistants will be used to manage tasks. AI will be used to improve customer service. Predictive analytics will be used to make decisions. It allows users to interact with digital objects using their smartphones. While mixed reality combines real world elements with virtual ones. These vehicles can drive on highways and through city streets without assistance from a driver or any human behind the wheel. They also protect organizations against malicious attacks. By 2030, this number is projected to grow to 3.1 billion. These people generate more than 70 percent of all greenhouse gas emissions and over 80 percent of global water usage.


MLOps Pays Dividends for New York Life

#artificialintelligence

Machine learning has the potential to generate millions of dollars in savings and revenue growth for organizations. But unless ML models are actually put into production, it's just a bunch of useless code. This is the big data science takeaway from New York Life, which recently adopted an MLOps solution from Domino Data Labs to streamline model deployment. Since it was founded in 1845, statistics have played a central role for New York Life. Like all life insurance companies, New York Life dedicates resources to maintaining accurate actuarial tables, which play a big role in determining premiums, payouts, and profits.


The New Intelligence Game

#artificialintelligence

The relevance of the video is that the browser identified the application being used by the IAI as Google Earth and, according to the OSC 2006 report, the Arabic-language caption reads Islamic Army in Iraq/The Military Engineering Unit – Preparations for Rocket Attack, the video was recorded in 5/1/2006, we provide, in Appendix A, a reproduction of the screenshot picture made available in the OSC report. Now, prior to the release of this video demonstration of the use of Google Earth to plan attacks, in accordance with the OSC 2006 report, in the OSC-monitored online forums, discussions took place on the use of Google Earth as a GEOINT tool for terrorist planning. On August 5, 2005 the user "Al-Illiktrony" posted a message to the Islamic Renewal Organization forum titled A Gift for the Mujahidin, a Program To Enable You to Watch Cities of the World Via Satellite, in this post the author dedicated Google Earth to the mujahidin brothers and to Shaykh Muhammad al-Mas'ari, the post was replied in the forum by "Al-Mushtaq al-Jannah" warning that Google programs retain complete information about their users. This is a relevant issue, however, there are two caveats, given the amount of Google Earth users, it may be difficult for Google to flag a jihadist using the functionality in time to prevent an attack plan, one possible solution would be for Google to flag computers based on searched websites and locations, for instance to flag computers that visit certain critical sites, but this is a problem when landmarks are used, furthermore, and this is the second caveat, one may not use one's own computer to produce the search or even mask the IP address. On October 3, 2005, as described in the OSC 2006 report, in a reply to a posting by Saddam Al-Arab on the Baghdad al-Rashid forum requesting the identification of a roughly sketched map, "Almuhannad" posted a link to a site that provided a free download of Google Earth, suggesting that the satellite imagery from Google's service could help identify the sketch.


Evaluation Methods and Measures for Causal Learning Algorithms

arXiv.org Artificial Intelligence

The convenient access to copious multi-faceted data has encouraged machine learning researchers to reconsider correlation-based learning and embrace the opportunity of causality-based learning, i.e., causal machine learning (causal learning). Recent years have therefore witnessed great effort in developing causal learning algorithms aiming to help AI achieve human-level intelligence. Due to the lack-of ground-truth data, one of the biggest challenges in current causal learning research is algorithm evaluations. This largely impedes the cross-pollination of AI and causal inference, and hinders the two fields to benefit from the advances of the other. To bridge from conventional causal inference (i.e., based on statistical methods) to causal learning with big data (i.e., the intersection of causal inference and machine learning), in this survey, we review commonly-used datasets, evaluation methods, and measures for causal learning using an evaluation pipeline similar to conventional machine learning. We focus on the two fundamental causal-inference tasks and causality-aware machine learning tasks. Limitations of current evaluation procedures are also discussed. We then examine popular causal inference tools/packages and conclude with primary challenges and opportunities for benchmarking causal learning algorithms in the era of big data. The survey seeks to bring to the forefront the urgency of developing publicly available benchmarks and consensus-building standards for causal learning evaluation with observational data. In doing so, we hope to broaden the discussions and facilitate collaboration to advance the innovation and application of causal learning.