Neurotechnology Launches StockGeist.ai, a New Platform that Enables Users to Monitor in Real-Time Hundreds of Publicly Traded Companies
The platform will be invaluable to investors and traders for real-time tracking of publicly traded companies based on the frequency of their mentions in social media posts along with major news, sentiment analysis and important corporate data.
Vilnius, Lithuania – October 7, 2020 – Neurotechnology, a provider of deep learning-based solutions and high-precision biometric identification technologies, today launched the new StockGeist.ai web platform for investors and traders to support aggregation and AI-based processing of information on publicly traded companies.
The platform uses deep learning models to provide a convenient, real-time monitor of the sentiment and context behind developments in the stock market as reflected in the media and social media. StockGeist.ai’s intuitive interface lets users quickly build customized watchlists with companies of interest for observing the dynamics in their ranking and other up-to-date information.
“With StockGeist.ai, you can quickly feel the spirit of the most recent developments in the business world,” said Dr. Vytautas Abramavicius, StockGeist.ai team lead from Neurotechnology. “Our web platform aggregates a tremendous amount of data from various media and social media sources. Using modern deep learning-based NLP (Natural Language Processing) models, StockGeist.ai allows users to derive meaningful insights from noisy data. It reflects our best efforts to bring this information to investors and traders in an intuitive and efficient way, so they can make faster decisions regarding stocks of interest.”
StockGeist.ai provides key information users need to know on the companies of their choice, including:
– Ranking: See the top 5 companies receiving the most attention in social media. Next to a ticker, the user can see the number of messages relating to that company and track the changes in its ranking.
– Watchlist: Build interactive charts from a selected set of tickers. For each ticker on the watchlist, the platform plots the total number of social media posts, cumulative message count, message ratio and positivity index.
– News Timeline: Create timelines of the public news related to a company of the user’s choice, including summaries of the most recent news. News stories are labeled by their sentiment as either positive, negative or neutral. StockGeist also offers a unique feature for displaying in color the sentiment span within the news summaries.
– Social Sentiment: Select a timeframe (5 minutes / hour / day) to see distribution plots of the social sentiment in the posts received within that timeframe. Two categories of informative and emotional messages are shown as separate plots.
– Wordcloud: Generate a wordcloud containing the most frequently encountered keyword pairs in social media will help the user quickly get a general idea of what the news is all about.
– Fundamentals: Gather important, relevant data on the fundamentals as well as general information on the company selected.
For more information about StockGeist.ai, please visit: www.stockgeist.ai.
Neurotechnology is a developer of high-precision algorithms and software based on deep neural networks and other AI-related technologies. The company was launched in 1990 in Vilnius, Lithuania, with the key idea of using neural networks for various applications, such as biometric person identification, computer vision, robotics and artificial intelligence. Since the first release of its fingerprint identification system in 1991, the company has delivered more than 200 products and version upgrades. More than 3,000 system integrators, security companies and hardware providers in more than 140 countries integrate Neurotechnology’s algorithms into their products. The company’s algorithms have achieved top results in independent technology evaluations, including NIST MINEX, PFT, FRVT, IREX and FVC-onGoing.
Preserving privacy of machine learning models
When you see headlines about artificial intelligence (AI) being used to detect health issues, that’s usually thanks to a hospital providing data to researchers. But such systems aren’t as robust as they could be, because such data is usually only taken from one organization.
Hospitals are understandably cautious about sharing data in a way that could get it leaked to competitors. Existing efforts to handle this issue include “federated learning” (FL) a technique that enables distributed clients to collaboratively learn a shared machine learning model while keeping their training data localized.
However, even the most cutting-edge FL methods have privacy concerns, since it’s possible to leak information about datasets using the trained model’s parameters or weights. Guaranteeing privacy in these circumstances generally requires skilled programmers to take significant time to tweak parameters – which isn’t practical for most organizations.
A team from MIT CSAIL thinks that medical organizations and others would benefit from their new system PrivacyFL, which serves as a real-world simulator for secure, privacy-preserving FL. Its key features include latency simulation, robustness to client departure, support for both centralized and decentralized learning, and configurable privacy and security mechanisms based on differential privacy and secure multiparty computation.
MIT principal research scientist Lalana Kagal says that simulators are essential for federated learning environments for several reasons.
- To evaluate accuracy. SKagal says such a system “should be able to simulate federated models and compare their accuracy with local models.”
- To evaluate total time taken. Communication between distant clients can become expensive. Simulations are useful for evaluating if client-client and client-server communications are beneficial.
- To evaluate approximate bounds on convergence and time taken for convergence.
- To simulate real-time dropouts. With PrivacyFL clients may drop out at any time.
Using the lessons learned with this simulator, the team we are in the process of developing an end-to-end federated learning system that can be used in real-world scenarios, For example, such a system could be used by collaborating hospitals to train privacy-preserving robust models to predict complex diseases.
Written by Adam Conner-Simons, MIT CSAIL
Oppo A33 (2020) With Triple Rear Cameras, 5,000mAh Battery Launched in India: Price, Specifications
Oppo A33 (2020) has been launched in India, featuring a large 5,000mAh battery, a a Qualcomm Snapdragon 460 SoC, and a hole-punch display with a 90Hz refresh rate. The smartphone also has a rear fingerprint scanner and a triple camera setup at the back. The Oppo A33 (2020) was unveiled in Indonesia last month, and will go on sale via Flipkart later this month though it is already available via offline retail stores, the company announced.
Oppo A33 (2020) price in India, launch offers (expected)
The Oppo A33 (2020) has been priced at Rs. 11,990 for its 3GB RAM + 32GB storage option. Oppo says it is available via offline retail stores, and will go on sale from Flipkart in its “next Big Billion Day sale.” Offers include 5 percent cashback on Kotak Bank, RBL Bank, Bank of Baroda, and Federal Bank cards. If users buy the phone from Paytm, benefits worth Rs. 40,000 will be listed. Offline, there are also going to be schemes options from banks like Bajaj Finserv, Home Credit, HDB Financial Services, IDFC First Bank, HDFC Bank, and ICICI Bank. The Oppo A33 (2020) was launched in Indonesia in September,
Oppo A33 (2020) specifications
The Oppo A33 (2020) runs on ColorOS 7.2 based on Android 10 and features a 6.5-inch HD+ (720×1,600 pixels) hole-punch display with 90Hz refresh rate. Under the hood, there is the octa-core Qualcomm Snapdragon 460 SoC. Internal storage is at 32GB with the option to expand further using a microSD card (up to 256GB).
The Oppo A33 (2020) smartphone also has the triple rear camera setup that includes a 13-megapixel primary sensor. The camera setup also has a 2-megapixel depth sensor and a 2-megapixel macro shooter. The Oppo A33 has an 8-megapixel selfie camera.
There is a 5,000mAh battery with 18W fast charging support on the Oppo A33 (2020). There is also the fingerprint sensor at the back of the handset. The phone also comes with dual stereo speakers. Connectivity options include Bluetooth v5, USB Type-C port, Wi-Fi 802.11ac, and more.
Should the government explain why Chinese apps were banned? We discussed this on Orbital, our weekly technology podcast, which you can subscribe to via Apple Podcasts, Google Podcasts, or RSS, download the episode, or just hit the play button below.
Big data and DevOps: No longer separate silos, and that’s a good thing
The pandemic has caused major shifts in the way IT and big data work. Now they may be working together for better outcomes.
The world has changed a lot since March 2020, and the coronavirus pandemic has affected nearly every aspect of our lives. While we’ve seen massive changes in technology already, another change happening right now is in big data and its role with DevOps.
“The COVID-19 pandemic has accelerated the blending of data analytics and DevOps, meaning developers, data scientists, and product managers will need to work more closely together than ever before,” said Bill Detwiler, editor in chief of TechRepublic.
SEE: TechRepublic Premium editorial calendar: IT policies, checklists, toolkits, and research for download (TechRepublic Premium)
Detwiler was interviewing managers at Tibco, a leader in big data integration and analytics. They said the coronavirus pandemic had caused organizations to rethink how they were using big data and analytics, generating what appears to be a movement toward merging IT DevOps methodologies with big data analytics.
For IT organizations, this is more than just a story about how the pandemic has altered how companies think about big data and analytics. The emergency of COVID has placed emphasis on getting analytics insights and results to market quickly. This has redefined analytics reporting as mission-critical, and not just as an ancillary tool for how companies operate and strategize.
SEE: Return to work: What the new normal will look like post-pandemic (free PDF) (TechRepublic)
The change is also creating revisions in operations and culture for IT. Here are some we’ve seen.
A move from waterfall to DevOps development
Developing, testing, and deploying big data applications is an iterative process. Because the process is iterative (i.e., develop-test-deploy until you get what you want), it doesn’t follow the more linear and assembly line-like development methodology of traditional IT waterfall development, which is a serial sequence of handoffs from development to QA (test) to an implementation staff.
SEE: Are you a big data laggard? Here’s how to catch up (TechRepublic)
A majority of IT departments are still organized around the waterfall development paradigm. There are separate silos within IT for development, testing, and deployment. These functions have to come together with each other and end users in the more collaborative and iterative process of big data application development. To do this, functional silos of expertise have to dissipate.
Culturally (and perhaps organizationally) this changes the orientation of IT. The culture shift is likely to entail the creation of interdisciplinary functional teams instead of work handoffs from functional silo to functional silo. End users also become active participants on these interdisciplinary teams.
Fewer absolutes for quality
The testing of big data applications becomes more relative and less absolute. This is a tough adjustment for IT because in traditional transaction systems, you either correctly move a data field from one place to another, or you obtain a value based on data and logic that absolutely conforms to what the test script dictates. If you don’t attain absolute conformance, you retest until you do.
SEE: Big data: How wide should your lens be? It depends on your use (TechRepublic)
Not so much with big data, which could start off with results being only 80% accurate, but with the business deeming them close enough to indicate an actionable trend.
Working in a context where less-than-perfect precision is acceptable is a challenging adjustment for IT pros, who are used to seeing an entire system blow up if a single character in a program or script is miskeyed.
The shift of big data into mission-critical systems
If you’re a transportation company, the ability to track your loads on the road and the health and safety of the cargo that they’re carrying becomes mission-critical. If you’re in the armed forces and you’re using drones on the battlefield to conduct and report reconnaissance in real-time flyovers, the data becomes mission-critical.
SEE: Big data success: Why desktop integration is key (TechRepublic)
This means that organizations must begin to attach the label of mission-critical to big data and analytics applications that formerly were classified as experimental.
IT culture must shift to support mission-critical big data applications for failover, priority maintenance, and continuous development. This could shift IT personnel from traditional transaction support to big data support, requiring retraining to facilitate the change.
- Technology5 months ago
First iPhone jailbreak in four years released
- Technology4 months ago
The Complete Guide for Building a Website
- Technology4 months ago
Check out the new Gaming Leader: Playstation 5
- Space5 months ago
NASA launches its First Space Flight in the U.S since 2011
- Technology3 months ago
Is OnePlus Nord the Best Phone Under Rs. 30,000?
- Politics3 months ago
US Politicians Considering to Ban TikTok App
- Entertainment3 months ago
Grimes Slams Baby Daddy Elon Musk After He Tweets ‘Pronouns Suck’
- Politics3 months ago
Beirut: How judges responded to warnings about ammonium nitrate stored at the Beirut port