I recently presented the plenary session at a pharma conference covering how Artificial Intelligence (AI) is transforming pharma sales and marketing, I provided examples Eularis had completed for pharma client projects. Several of the attendees sent me emails afterwards wanting to know more about the specific examples I gave, which were as varied as our client needs. It was interesting to learn how few of these types of applications they were familiar with, and I thought the readers of my white papers would want to know about them, too. I've written about many of these topics before, and I'm including those links at the end of each section in case you are interested in digging deeper into a specific topic. According to Takeda Pharmaceuticals, the average time taken to diagnose a rare disease without technology is 7.6 years and comes after countless tests and physician visits. This creates a high cost to the healthcare system, not to mention much suffering for the patient. And some cases are even worse.

Coastal communities around the world are especially vulnerable to flooding, storms, hurricanes and heavy rainfall. Now, scientists are studying whether artificial intelligence can better predict the impact of the storms. More information would help areas like New Orleans, Louisiana, which is forced to fix and rebuild after severe flooding. Clint Dawson, a professor at the University of Texas Austin, is part of a team of investigators working on a project funded by the Department of Energy's Office of Advanced Scientific Computing Research. "The only reason that place still exists is because there is fairly extensive levy system that protects it," Dawson said.

An oncoming tsunami of data threatens to overwhelm huge data-rich research projects on such areas that range from the tiny neutrino to an exploding supernova, as well as the mysteries deep within the brain. When LIGO picks up a gravitational-wave signal from a distant collision of black holes and neutron stars, a clock starts ticking for capturing the earliest possible light that may accompany them: time is of the essence in this race. Data collected from electrical sensors monitoring brain activity are outpacing computing capacity. Information from the Large Hadron Collider (LHC)'s smashed particle beams will soon exceed 1 petabit per second. To tackle this approaching data bottleneck in real-time, a team of researchers from nine institutions led by the University of Washington, including MIT, has received $15 million in funding to establish the Accelerated AI Algorithms for Data-Driven Discovery (A3D3) Institute.

Machine learning has found wide success across a wide variety of fields. In order to understand different ML algorithms, it becomes important to understand the different data types and how they are preprocessed before training models on them. To understand the different data types found in machine learning, read this blog.

The graph represents a network of 6,501 Twitter users whose tweets in the requested range contained "chatbot", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 28 January 2022 at 13:30 UTC. The requested start date was Friday, 28 January 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 7-day, 17-hour, 41-minute period from Thursday, 20 January 2022 at 07:18 UTC to Friday, 28 January 2022 at 01:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

Artificial super-intelligence is seen as a by-product of accomplishing the goal of AGI. A commonly held belief by insiders is that general intelligence will trigger an "intelligence explosion" that will rapidly trigger what is referred to as artificial super-intelligence, or ASI. It is thought that ASI is "possible" due to recursive self-improvement, the limits of which are bounded only by a program's imagination. This so-called intelligence explosion is often associated with a technological singularity. The singularity accelerates to meet and quickly surpass the collective intelligence of all humankind.

I could spend hours discussing early-stage startup operations and community-based marketing, but deal flow is my blind spot. But when investment banking firm UBS picked up financial robot-advisor Wealthfront for $1.4 billion in an all-cash deal this week, I noticed. "At those prices, the company's exit price is a win in that it represents a 2x or greater multiple on its final private valuations," wrote Alex Wilhelm in The Exchange. "But its exit value is also parsable from a number of alternative perspectives: AUM, customers and revenue," he added. Examining each of those factors in turn, Alex found that the deal is more than just a "next-gen push" intended "to reach rich young Americans," as some headlines suggested.

When software providers talk about the technologies they say "democratize" AI, they also talk a lot about "guardrails." That's because the rapidly evolving world of AI tools is still more like a republic governed by the machine-learning elite. Although no-code and low-code AI tools promise to give everyone a chance to build business analytics models or simple applications that use AI to complete tedious tasks, the amateurs whom no-code AI companies refer to as "citizen data scientists" are often required to play with the bumper rails up. That's because toolmakers and management are worried about the risks inherent in allowing just anyone to create sophisticated AI systems. "As you go into low-code and actually more the no-code environment, then there are guardrails as to what you can and can't do," said Ed Abbo, president and chief technology officer at C3 AI, which provides software designed to help people with zero coding experience build machine learning models.

Read the e-book, Engineering MLOps, to gain a complete understanding of how MLOps works, explore real-world use cases, and see how to implement MLOps in your organization for your machine learning and AI solutions.

Job description Artificial intelligence, machine learning and digital twins have the potential to transform cardiology. A cardiac digital twin is the computational replica of the cardiac system of a specific patient. The digital twin provides an unprecedented ability to both depict an integrated and comprehensive diagnostic picture, and to predict the prognosis under a range of therapeutic strategies. We are seeking to appoint a data scientist/engineer to develop and apply the technology that allows the creation of cardiac digital twins at scale. The successful candidate will develop and apply state of the art machine learning and data assimilation methods to automatically analyse longitudinal patient data that will be encoded in a digital twin of the patient's heart.