If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Artificial Intelligence is here to stay. The development of AI is speeding up on a daily basis. Only recently, Google's DeepMind created the AI AlphaStar that secured a decisive victory against two grandmaster players of the game of StarCraft II. In a series of test matches they played, the algorithm won 5-0. This victory is a decisive moment for artificial intelligence, as the game of StarCraft II is fundamentally more difficult than the other games where Deepmind's algorithm already claimed victory.
In a major step toward autonomous healthcare, a robot successfully completed laparoscopic surgery on a pig without human backup. Designed by a team of Johns Hopkins University researchers, the Smart Tissue Autonomous Robot (STAR) has performed the procedure--which requires a high level of repetitive motion and precision--in four animals, producing "significantly better" results than humans. "Our findings show that we can automate one of the most intricate and delicate tasks in surgery: the reconnection of two ends of an intestine," according to Axel Krieger, assistant professor of mechanical engineering at JHU's Whiting School of Engineering. Even the slightest hand tremor or misplaced stitch can lead to catastrophic complications. Krieger, in collaboration with the Children's National Hospital in Washington, D.C., and Hopkins professor of electrical and computer engineering Jin Kang, designed and built the vision-guided robot specifically to suture soft tissue.
Companies and investors will find valuable AI/ML software across all three layers. At Insight, we initially focused on layers two and three. We invested in startups creating robust ML systems that addressed specific problems, either vertically (like credit underwriting company Zest AI) or horizontally (like cybersecurity company SentinelOne). We thought that economic moats were hardest to build at layer one; in part as a result of robust open source ecosystems and because large public cloud vendors deliver many of these tools at low prices.
TF-IDF (Term Frequency-Inverse Document Frequency) is a way of measuring how relevant a word is to a document in a collection of documents. TF-IDF has many uses, such as in information retrieval, text analysis, keyword extraction, and as a way of obtaining numeric features from text for machine learning algorithms. TF-IDF was first designed for document search and information retrieval, where a query is run and the system has to find the most relevant documents. Suppose the query is the text "The bug". The system would give each document a higher score proportionally to the frequencies of the query words found in the document, weighting more rare words like "bug" with respect to common words like "the".
When humanity contemplates sending assets to other planets, what should be our goal? The choice is between taking pride in what nature manufactured over 4.5 billion years on Earth through unsupervised evolution and natural selection, or aspiring to a more intelligent form of supervised evolution elsewhere. The first choice -- AI -- is apt to an industrial duplication line, for which the proof of concept for the assembly line was already demonstrated on Earth and we can duplicate it in an Earth-like environment. We are emotionally attracted to the second choice, because we are attached to ourselves and our natural path for maintaining the longevity of our genetic-making through biological reproduction. Prioritizing the natural processes of the second choice is misguided for two reasons.
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.