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Why Advisors Are Still a Long Way From Embracing AI: Schwab

#artificialintelligence

Conversational artificial intelligence, natural language processing and voice could be the next disruptive technologies in the financial services industry, but there continue to be barriers to advisors and clients of theirs and other financial institutions accepting these technologies, according to Kyle Caffey, managing director of conversational AI at Charles Schwab, and Swapna Malekar, product lead at RBC. "Financial use cases can be more complicated, and so delivering a good client experience could certainly be a part of that adoption barrier," Caffey said last week while speaking during a "Future Focus" session at the Finovate Fall Digital online conference. Another big issue is that "the nature of the data that we're relying on and passing through some of these technologies is sensitive," he said, underscoring the importance of risk management. Schwab has been careful with privacy and the "security of our clients' data as we pursue some of these technologies -- and that's something that we've been very mindful [of] as we've executed on our vision and road map, and it's something for any financial services firm or bank that's considering deploying conversational AI … to make sure they're paying attention to," he said. In most cases, business use cases for technology are "built on either a revenue plan or an expense savings plan," he noted, adding: "Certainly, conversational AI brings to bear capabilities on both sides of the business case." But Schwab "really started with the client experience and thought about'how do we make our digital ecosystem as accessible and simple for our clients [as possible] to get them what they're looking to get done?'"


Facebook AI Open Sources DEtection TRansformer (DETR)

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Every once in a while, a machine learning framework or library changes the landscape of the field. In this article, we'll quickly understand the concept of object detection and then dive straight into DETR and what it brings to the table. In Computer Vision, object detection is a task where we want our model to distinguish the foreground objects from the background and predict the locations and the categories for the objects present in the image. Current deep learning approaches attempt to solve the task of object detection either as a classification problem or as a regression problem or both. For example, in the RCNN algorithm, several regions of interest are identified from the input image.


Who is this "We" you speak of? -- Joe's Notes

#artificialintelligence

I am not in control of others. I am barely in control of myself. The only way to control others is through force, everything else is a negotiation. When I listen to certain people prognosticate about what we all are doing or should be doing I can't help but become curious about what they mean by "we". "We" is a collective noun that takes on different forms depending on the context it is used in. A Christian can address a group of Christians and say the word "we" and it is to whom they are referring.


Drones – the New Critical Infrastructure

#artificialintelligence

Be prepared in the near future when you gaze into the blue skies to perceive a whole series of strange-looking things – no, they will not be birds, nor planes, or even superman. They may be temporarily, and in some cases startlingly mistaken as UFOs, given their bizarre and ominous appearance. But, in due course, they will become recognized as valuable objects of a new era of human-made flying machines, intended to serve a broad range of missions and objectives. Many such applications are already incorporated and well entrenched in serving essential functions for extending capabilities in our vital infrastructures such as transportation, utilities, the electric grid, agriculture, emergency services, and many others. Rapidly advancing technologies have made possible the dramatic capabilities of unmanned aerial vehicles (UAV/drones) to uniquely perform various functions that were inconceivable a mere few years ago.


Will AutoML software replace Data Scientists?

#artificialintelligence

AutoML is a generic expression to indicate pieces of software that perform Machine Learning tasks automatically. Such pieces of software can be Python libraries like Auto-Sklearn or software programs like Data Robot. AutoML pieces of software replace all the boring steps that take more time to a Data Scientist's work. They actually make all the combinations of the several parameters of a pipeline (e.g. the blank filling values, scaling algorithm, model type, model hyperparameters) and select the best combination that maximizes some performance metrics (like RMSE or Area under the ROC Curve) in k-fold cross-validation using some search algorithm (like Grid or Random Search). They can really simplify the life of somebody that has to create a model from scratch and sometimes they explore combinations and scenarios that a Data Scientist may not have thought of.


Various Types Training a Machine to become intelligence

#artificialintelligence

In the field of machine learning based on the condition of learning classified into three types. In this phase we teach or train the machine using data ie: information which is well labeled that means some data is already have with the correct answer. In this phase, the machine is provided with the new set of example ie: data so that machine analyses the training data (set of training example) and produces a correct outcome from the labeled data. Here the name itself indicates the presence of supervisor as a teacher. Here certain technical parameter which is ease in understanding.


This AI Turns Celebrities into Incredible Cartoon Characters - Nerdist

#artificialintelligence

Doron Adler and Justin Pinkney, two software engineers, recently released a "Toonification translation" AI model that turns real faces into flawless cartoon representations. And while the toonification tool, "Toonify," was originally available to the public, it became too popular to sustain cheaply. But some people managed to Toonify a ton of celebrities before the tool was pulled, and all the animations are stellar. After much training of neural networks @Norod78 and I have put together a website where anyone can #toonify themselves using deep learning!https://t.co/OQ23p30isC In a series of blog posts, which come via Gizmodo, Pinkney outlines how he and Adler created Toonify.


How AI can transform healthcare

#artificialintelligence

Artificial intelligence is driving changes in almost every industry, healthcare included. The cost of healthcare has been rising rapidly for decades on end. Some studies have concluded that healthcare will account for over 20% of the GDP of the US by 2025. At the same time, healthcare professionals are working hard to treat the increasing number of patients with their high patient care expectations. Artificial intelligence could be the solution that the industry is desperately searching for.


How 'Microsoft Flight Simulator' became a 'living game' with Azure AI

Engadget

Microsoft Flight Simulator is a triumph, one that fully captures the meditative experience of soaring through the clouds. But to bring the game to life, Microsoft and developer Asobo Studio needed more than an upgraded graphics engine to make its planes look more realistic. They needed a way to let you believably fly anywhere on the planet, with true-to-life topography and 3D models for almost everything you see, something that's especially difficult in dense cities. A task like that would be practically impossible to accomplish by hand. But it's the sort of large-scale data processing that Microsoft's Azure AI was built for.


US military is testing wearable devices that detects illnesses TWO days before symptoms arise

Daily Mail - Science & tech

The US military is testing a smart watch and ring system capable of detecting illnesses two days before the wearer develops symptoms. Called Rapid Analysis of Threat Exposure (RATE), the project is using Garmin and Oura devices that have been program with artificial intelligence trained on nearly 250,000 coronavirus cases and other sicknesses. The system notifies the user of an oncoming illness using a scale from one to 100 on how likely it will happen over the next 48 hours. Military officials note that'Within two weeks of us going live we had our first successful COVID-19 detect.' The US military is testing a smart watch and ring system capable of detecting illness two days before the wearer develops symptoms.