Artificial Intelligence Powered Chatbot Will Perform Upto 200 Banking Tasks
The Commonwealth Bank of Australia unveiled its own chatbot, called Ceba. The bank described it as a “talking chatbot” which could handle phone requests to perform up to 200 banking tasks, including answering queries and making bill payments. The bank claims that Ceba will deal with requests such as “Can I have a copy of my statement?”, “I want to open a new account” and “What is my credit card limit?” with efficiency. The chatbot uses artificial intelligence (AI) to assist customers with more than 200 banking tasks, including activating a card, checking an account balance, paying a bill, or getting cardless cash. This gives the administration of the bank a huge relief from rudimentary mundane tasks which are repeated multiple times in a single day. Rolling out to more than 6.2 million NetBank and CommBank app customers in the coming weeks, Ceba will help them do all this with its help. The bot, available 24/7, can recognize approximately 60,000 different ways customers ask for banking tasks and will eventually be able to tell users what they are spending their money on.
New AI System Predicts How Long Patients Will Live
A research team from Stanford University is hoping to improve the timing of end-of-life care for critically ill patients. They have developed an artificially intelligent algorithm to predict patient mortality. Doctors must consider an array of complex factors before predicting mortality. Sometimes doctors can be off by several months, both in terms of predicting death too late or too early. This poses a problem for the accurate scheduling of palliative care. If a patient is transitioned to palliative care too late, they’re likely to miss out on this important stage of care. And if they’re admitted too early, it places an unnecessary strain on the healthcare system. In tests, the system developed by them proved eerily accurate, correctly predicting mortality outcomes in 90 percent of cases. The system uses a form of AI known as deep learning, where a neural network learns from large amounts of data. The system was fed data from the electronic health records (EHR) of adult and child patients admitted. After parsing through 2 million records, the researchers identified 200,000 patients suitable for the project. The deep learning algorithm studied the case reports from 160,000 of these patients, and was given the directive: “Given a patient and a date, predict the mortality of that patient within 12 months from that date, using EHR data of that patient from the prior year.” The system was trained to predict patient mortality within the next three to 12 months.
AI Enabled ‘Drawing Bot’ to Create Images from Text Descriptions
Microsoft researchers are developing an AI enabled ‘drawing bot’ that can create images from text descriptions of an object. The technology can generate images of everything from ordinary pastoral scenes, such as grazing livestock, to the absurd, such as a floating double-decker bus. Each image contains details that are absent from the text descriptions, indicating that this AI contains an artificial imagination. Text-to-image generation technology could find practical applications acting as a sort of sketch assistant to painters and interior designers, or as a tool for voice-activated photo refinement, the researchers said. At the core of Microsoft’s drawing bot is a technology known as a generative adversarial network, or GAN. The network consists of two machine learning models, one that generates images from text descriptions and another, known as a discriminator, that uses text descriptions to judge the authenticity of generated images.
New AutoML Service Promises Code-Free AI Development
AI has become quite accessible for enterprises thanks to cloud services. Google is taking it one step further by building a custom AI optimized for a company’s specific use. Google introduced a new cloud toolkit called AutoML that provides a drag-and-drop interface for training AI models. According to Google, the service aims to address the fact that most enterprises can’t afford the talent necessary to build a machine learning model from scratch. Plus, AI development is often taxing even for the organizations that can. The first tool in the AutoML line-up is called AutoML Vision. It’s designed to ease the creation of models that process images. To start a new project, a user must upload a sample dataset reflective of the photos that the AI is expected to process. It’s also necessary to tag each image with appropriate keywords — so that the model will make the necessary associations. The user then simply needs to hit the “train” button in the interface and the service will take it from there. One early adopter of AutoML is Urban Outfitters Inc.’s data science team. The retailer is employing the service to automatically extract attributes such as neckline style from fashion photos, data that it uses to help online shoppers search for items more easily.
AI-Powered Amazon Go Store - A Cashier-less Store Opened for Public
Amazon Go, the brand-new no-cash, no-credit-card, no-checkout convenience store will be opened to public in Seattle. The tech giant first introduced the Amazon Go store concept in 2016. Customers must scan the Amazon Go app upon entering the store. Sensors will then track their movements and charge shoppers' Amazon (AMZN) accounts for the items they grab. Customers just walk out of the store. Items scanned are charged to the customer’s Amazon account when they leave with their goods. No cashiers needed. Amazon Go's offerings include groceries, ready-to-eat meals, cold drinks and meal prep kits.
Federated Learning Eliminates the Need to Store & Train Data in One Place
There are primarily two challenges in applying machine learning to drug development; small and dispersed data sets and privacy issues. Owkin, a predictive analytics firm, aims to overcome them through transfer and federated learning. The company builds machine-learning models and algorithms that analyze molecular and medical imaging libraries as well as patient profiles to uncover complex biomarker patterns behind diseases. The firm utilizes two machine learning tools—transfer learning and federated learning. Transfer learning improves learning capacities on one data set or project through knowledge gathered from another related task. This allows machine learning to be applicable in smaller data sets. Federated learning takes out the requirement of storing and training data in one place (usually in the cloud). Because healthcare data are extremely private, sharing them with outsiders is one of the major bottlenecks for applying AI in healthcare. The method facilitates cooperation between healthcare providers and biopharma companies without the concern of data privacy.
AI Powered, Cloud-Based Electronic Health Record Software to Help Doctors
eClinicalWorks, the company, will be focusing on “elevating an EHR from the traditional mindset of data entry to smartness and intelligence that helps doctors,” said CEO Girish Navani. Three examples are the Open and Connected Office, Virtual reality and precision medicine features and functionality within the EHR (electronic health record). “Not only do we have the ability to order that test as easily as doctors can order a blood test but going forward the lab will send the results back into the EHR,” Navani added. “At the point of entry, the physician writes an order, like a medication, and it validates that against the genetic profile.” On the AI front, new iteration also includes Eva, a virtual reality assistant. Upon calling, Eva responds with choices pertaining to tasks doctors typically do during an office visit, such as select a patient by name, send messages without interrupting the workflow, open a progress note or to add a new diagnosis to the problem list. “This is like the ATM network. But you don’t have to pay that $3 transaction fee,” Navani said.
Machine Learning to Help B2B Firms Learn About Their Customers
Extensive data and advanced analytics for B2C have enabled companies to better understand consumer behavior. Now, selling product and service offerings to business customers is experiencing increased focus with the better availability of new digital data that describes businesses. Neural networks and “deep learning” algorithms, enable data scientists to mine the gold in digital formats. These AI-based methods involve advanced search techniques that identify, categorize, and gather user-defined data elements corresponding to search criteria. Well-designed AI-based algorithms are the key to extracting information from social networking sites like LinkedIn etc. These structured data resources provide means for yet another application of AI-based algorithms, where focus is on identifying patterns in data that ultimately provide the basis for predictive sales and marketing models. These can be used for scoring, forecasting, and classification capabilities. Machine learning is used to identify, extract, and model a categorization scheme of companies so that users in the B2B space can more accurately identify opportunities.
AI to Offer Tailormade Experience to Hotel Guests
Hotels are hoping to use AI to get better knowledge of their clients via personal data provided on reservation. The tourism sector is starting to embrace new technologies, hoping to benefit from lucrative personal data. In a prototype of the hotel of the future on display at Madrid’s Fitur tourism fair, receptionists have disappeared, and customers are checked-in via a mirror equipped with facial recognition. Once the client is identified, the room adapts itself automatically to all demands made at reservation: temperature, lighting, Picasso or Van Gogh in the digital frames hanging on the walls. The room prototype put on show by French technology consultancy Altran, aimed at luxury hotels, has incorporated cutting-edge speech recognition technology. The mattress is equipped with sensors and records the movements of those sleeping, which could prompt hotel staff to offer them a coffee when they wake up. Fed with this data, AI algorithms will get to work, determining what the clients’ habits are to lure them back again by offering a tailor-made experience, or sell them additional products.