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Artificial intelligence and machine learning are increasingly embraced by U.S. carriers as they seek to remain competitive and modernize their operations, a new LexisNexis Risk Solutions study has found. Struggles remain, however, in terms of figuring out staffing and proper use of the technology to optimize its benefits. LexisNexis' look at how the top 100 U.S. carriers are using and benefiting from artificial intelligence and machine learning found a robust adoption of the technology and a strong belief in the benefits it will bring. Approximately 62 percent of respondents said they worked for insurance carriers that have already adopted artificial intelligence (AI) and machine learning (ML) initiatives. About 75 percent said they believe AI and ML can provide carriers with a competitive advantage through better decision-making.
At a technical level, artificial intelligence seems to be the future of software. AI is showing remarkable progress on a range of difficult computer science problems, and the job of software developers – who now work with data as much as source code – is changing fundamentally in the process. Many AI companies (and investors) are betting that this relationship will extend beyond just technology – that AI businesses will resemble traditional software companies as well. Based on our experience working with AI companies, we're not so sure. We are huge believers in the power of AI to transform business: We've put our money behind that thesis, and we will continue to invest heavily in both applied AI companies and AI infrastructure. However, we have noticed in many cases that AI companies simply don't have the same economic construction as software businesses. At times, they can even look more like traditional services companies. Anecdotally, we have seen a surprisingly consistent pattern in the financial data of AI companies, with gross margins often in the 50-60% range – well below the 60-80% benchmark for comparable SaaS businesses.
"It's machine learning's job to find patterns based on the data you give it to help you focus on the data points most likely to lead to conversion." Elizabeth Gallagher, chief revenue officer at Lineate talks about how machine learning (ML) and artificial intelligence (AI) are changing the game for ecommerce brands. With the use of predictive analytics, marketers can create personalized marketing campaigns. In this edition of MarTalk Connect, Gallagher shares the key data points marketers should use to provide personalized recommendations. She stresses how data-driven automation and machine learning are strategic assets to enhance the customer journey.
The bouquet of AI, pushed by machine learning, computer vision and the Internet of Things (IoT), is speedily evolving as a significant universal purpose technology. Besides technology companies, it is currently being pursued across sectors ranging from manufacturing, agriculture, healthcare, retail, financial services, banking, national defence, and security to public utilities. "We encourage our engineers in India to constantly push the boundaries of AI and machine learning capabilities, with applications from risk, marketing, customer service to autonomous infrastructure...," said Jayanthi Vaidyanathan – Senior Director Human Resources, PayPal India. "We have formulated several Leadership programs to build mid and senior leadership; programs that focus on soft skills of the individuals be it in influencing, brand building, communication, to name a few and also a structured job rotation program to continuously create opportunities for the top talent to diversify and equip themselves with newer skills," she said. The Ministry of Commerce and Industry constituted a task force in 2018 to study'How AI is reshaping jobs in India'.
How well do Robinhood's financials stack up against incumbent online brokerages? While we wait for the seven-year-old company's long-planned IPO, Alex Wilhelm examined Morgan Stanley's big $13 billion purchase of E-Trade for fresh data comparison points. Robinhood has 10 million accounts -- twice what E-Trade has -- but it also appears to make much less money per user and has far fewer assets under management, as he covered for Extra Crunch. So while its fee-free approach has destroyed a key revenue stream for competitors, it still has to grow its own "order-flow" business into its private-market valuation. One solution is to make the platform stickier via social features.
Implementation of the "Internet of Things" in the modern world is gaining pace at breakneck speed. Society is moving away from standalone devices and entering the realm of inter-connectivity. With uses in different facets of life, such as personal gadgets, retail, electricity distribution and financial services, IoT is making its mark. One such application field of IoT is in Smart Homes, or more specifically in the Heating, Ventilation, and Air Conditioning industry (HVAC). According to a report by Zion Market Research, the global smart HVAC control market is expected to reach almost USD 28.3 billion by 2025 as compared to USD 8.3 billion in 2018.
For this post, we measured fine tuning performance (training and inference) for the BERT (Bidirectional Encoder Representations from Transformers) implementation in TensorFlow using NVIDIA Quadro RTX 8000 GPUs. For testing, we used an Exxact Valence Workstation fitted with 4x Quadro RTX 8000's with NVLink, giving us 192 GB of GPU memory for our system. These tests measure performance for a popular use case for BERT and NLP in general, and are meant to show typical GPU performance for such a task. We made slight modifications to the training benchmark script to get the larger batch size metrics. The script runs multiple tests on the SQuAD v1.1 dataset using batch sizes 1, 2, 4, 8, 16, 32, and 64 for training, and 1, 2, 4, and 8 for inference.
When Stephen Hawking warned of the dangers of Artificial Intelligence in 2015, his concerns were about the Superhuman AI that would pose an existential risk to humanity. But in recent years, much more imminent danger of AI has emerged that even a genius like Hawking could not have predicted. Deepfakes depict people in videos they never appeared in, saying things they never said and doing things they never really did. Some of the harmless ones have the actor Nicolas Cage's face superimposed on his Hollywood's peers while the more serious and dangerous ones target politicians like the US House Speaker Nancy Pelosi. Deeptrace, a cybersecurity startup based in Amsterdam found 14,698 deepfakes in June and July, an 84% increase since December of 2018 when the number of AI-manipulated videos was 7,964.