Artificial Intelligence is no new concept. The phrase was first coined by John McCarthy in 1956, when he invited a group of researchers to discuss the notion of'thinking machines' during a conference at Dartmouth College. Since then, it has been a point of fascination for scientists, academics, software developers, and moviemakers alike. Fast-forward to today where you'll find lots of examples hiding in plain sight. From digital assistants like Amazon's Alexa or Apple's Siri, who use AI to learn from user interactions, to automated email responses and search engines predicting what you're looking for.
This series defines that environment & provides a framework to align current efforts with a 2.0 Future. What are the 2.0 Underwriting Requirements? How are new data sources, machine learning and AI, and RPA automation being used to address them? How does that change digital transformation efforts. One of InsurTech's top influencers, author, speaker and consultant in connected insurance, innovation, transformation and leadership.
Edge AI chip startup Deep Vision has raised $35 million in a series B round of funding led by Tiger Global, joined by existing investors Exfinity Venture Partners, Silicon Motion and Western Digital. The company began shipping its first-generation chip last year. ARA-1 is designed for power-efficient, low-latency edge AI processing in applications like smart retail, smart city and robotics. While the company's name suggests a focus on convolutional neural networks, ARA-1 can also accelerate natural language processing with support for complex networks such as long short-term memory (LSTMs) and recurrent neural networks (RNNs). A second-generation chip, ARA-2 with additional features for accelerating LSTMs and RNNs will launch next year.
Major disruptive technologies such as artificial intelligence have penetrated into the global tech market to enhance productivity for better yield. Organizations and factories are instigated to implement multiple AI models for automating tasks and generating meaningful in-depth insights. But this increase in human-machine interaction has created a concern among human employees, especially those who are elders in the workplace. How will the human employees earn to live a standard life? These types of questions are coming into the minds of people after robots and AI models are automating processes and dealing with loads of data efficiently and effectively. Do we really have a backup plan if seriously artificial intelligence causes unemployment in the next few years?
Fueled with a $36 million grant from Lilly Endowment Inc., the Central Indiana Corporate Partnership has launched an initiative called AnalytiXIN to promote innovations in data science throughout Indiana. Build connections between Indiana's manufacturing and life sciences companies and the university researchers who can help them use artificial intelligence and advanced data analytics to tackle big challenges like reducing a factory's carbon footprint or improving worker health. "This is one way to ensure early that these kinds of critical collaborations are happening," said David Johnson, president and CEO of the Indianapolis-based Central Indiana Corporate Partnership. About half of the $36 million will be used to hire university-level data-science researchers, some of whom will be based at 16 Tech in Indianapolis. The other half will go toward the creation of "data lakes," or large data sets built from information from multiple contributors.
The term'Artificial Intelligence' or AI has been in vogue for quite some time. We have usually seen the use of AI in science fiction movies, and the effects are intimidating. Artificial Intelligence continues to be a hot topic, and with the advancement of technology, AI is improving every day and has entered our lives. Talking about the insurance sector, most insurance policies, including term insurance plans globally, continue to record relatively modest numbers since some of the market segments are largely under-penetrated. But the scenario is gradually changing as people are becoming aware of the importance of insurance in their everyday living.
First, technology always creates winners and losers. While automation poses risks to some workers and firms, for others – especially in developing countries – it presents opportunities to upgrade quality, reach new export markets, and create productive employment. Policy should allow such firms to grow, for example by lowering labor market rigidities, which may help to offset any negative effects of declining firms and sectors. Second, our findings warn that growing trade protectionism may slow cross-border technology diffusion. It may constrain the ability of firms in developing countries to upgrade production processes, move into higher value-added activities and produce the high-quality products demanded by consumers.
Global pandemic limitations have had a direct influence on traditional real estate processes – and for the better, unexpectedly. Thousands of businesses, realtors, appraisers, mortgage lenders, and others have been forced to manage the crisis by incorporating rapidly emerging PropTech, and with good cause. Real estate AI apps can manage predetermined data flows, learn user behavior, streamline and speed operations, and allow more accurate assessments and market forecasts in the short term. Real estate AI apps are being embraced by homeowners, potential renters, and purchasers, and investors are aware that real estate is the world's greatest asset class. These top 10 AI apps help real estate professionals interact with prospects more rapidly, boost sales, manage renters and properties, and more.
This dreadful September could be stuck in neutral. After the market saw a bit of a bounce on Monday, Tuesday, the Dow fell nearly 300 points, with broad losses across the other indices. The S&P 500 closed Tuesday at its lowest since Aug. 20, the Nasdaq NDAQ continued a 5-day losing streak, and the Dow, S&P 500, and Russell 2000 saw red for the sixth time in the last seven days. Investors continued to worry about how the delta variant could derail the economic recovery, along with worries about what moves the Fed could make. Inflation continues to be a concern, too.
From data centers, through edge accelerators to endpoint devices: Artificial intelligence (AI) Applications range from large scale analysis of medical data and online retail recommendation engines, to robotics and computer vision, to sensor fusion in the tiniest sensor nodes. The infusion of AI techniques into so many areas of computing is changing compute paradigms across the board. Our Virtual Event will provide answers to questions like: How to keep up with these changes, especially given AI's propensity to evolve at a staggering rate? How does one design chips or systems for a constantly shifting workload like this? How does one make the call between maximising performance today and keeping some flexibility for the sake of future-proofing? AI in the Data Center AI in the data center is revolutionising online retail in the cloud and applications like medical imaging and the financial sector at the enterprise level.