The Artificial Intelligence and Machine Learning News clippings for Quants are provided algorithmically with CloudQuant’s NLP engine which seeks out articles relevant to our community and ranks them by our proprietary interest score. After all, shouldn’t you expect to see the news generated using AI?
AI defeats human F-16 pilot in virtual dogfight
Over the course of five battles, Heron’s AI program defeated the master pilot as part of a competition run by DARPA.
An artificial intelligence program has defeated a US fighter pilot in five rounds of simulated aerial dogfights.
The program beat the F-16 US Air Force pilot, known only as Banger, in each round as part of a competition hosted by the Pentagon’s Defence Advanced Research Projects Agency (DARPA).
The competition, which started with eight teams of various AI software, was streamed on YouTube overnight.
2020-08-21 00:00:00 Read the full story…
Weighted Interest Score: 2.4894, Raw Interest Score: 0.9868,
Positive Sentiment: 0.1880, Negative Sentiment 0.2820
CloudQuant Thoughts : In the intro one of the pilots talks about a maneuver that the AI makes that no human would make because it is uncomfortable.. an increase in speed and pushing the nose down, causes a lift in the diaphragm and this pilot being tall hits his head on the canopy. I thought it was a very interesting tidbit.
Data Science Salaries Are Flat, But Analytics Teams Weather Pandemic
For all the clamor about the shortage of data scientists, an analysis of median salaries for data crunchers shows only a slight increase in salaries over the previous year.
The latest Burch Works study of salaries for data scientists and predictive analytics professionals found a mere 1 percent increase in media pay over the previous year. Median entry level salaries settled at $80,000, with increases based on job level rising to $135,000. Managerial salaries ranged as high as $250,000.
Overall, salaries for data scientists and analysts was flat when compared to last year. “Salaries remained fairly steady, either showing no change or increasing slightly,” Burch Works reported on Thursday (Aug. 20.).
2020-08-20 00:00:00 Read the full story…
Weighted Interest Score: 3.0725, Raw Interest Score: 1.8221,
Positive Sentiment: 0.1429, Negative Sentiment 0.1786
CloudQuant Thoughts : Probably not surprising as everyone just attempts to sit the pandemic out!
8 Fun AI Tools Available Online
“AI for fun” — a phrase that we commonly don’t hear in the industry. Artificial intelligence has always been considered a revolutionary technology that has emerged to solve complex real-world problems like high-level computation, omitting manual labour, or data-driven optimisation. However, with its endless possibilities, there are many applications of AI that make this technology more accessible to the average layman person or kids at home.
To get people’s head around this sophisticated technology developers all around the world are continuously developing some fun AI tools that can be easily accessed online to get hands-on. Not only are these AI tools fun but also provide a good understanding of this technology to the users.
Here is a list of 10 exciting artificial intelligence tools that are available online for anyone to have fun with.
- Quick, Draw!
- Even Stranger Things
- Scribbling Speech
- Thing Translator
2020-08-23 10:30:00+00:00 Read the full story…
Weighted Interest Score: 3.0512, Raw Interest Score: 1.2498,
Positive Sentiment: 0.1538, Negative Sentiment 0.0577
CloudQuant Thoughts : Always nice to add a little fun to the mix.
DARPA Chip Effort Advances AI Hardware
Projects Agency’s Electronics Resurgence Initiative (ERI) includes development efforts aimed at AI hardware components needed to provide the computational horsepower for accelerating the movement of big data used in emerging machine learning applications.
“U.S. leadership in microelectronics is essential to U.S. leadership in artificial intelligence,” Gilman Louie, a member of the National Security Commission on Artificial Intelligence (NSCAI), told this week’s virtual ERI conference. Maintaining the lead in AI hardware requires “technical feats only DARPA would attempt.”
In a series of reports to Congress, the commission has emphasized continued U.S. leadership in microelectronics as a way to “get AI right,” said Louie, founder and former CEO of In-Q-Tel, the venture arm of the U.S. intelligence community.
NSCAI, which is led by former Google CEO Eric Schmidt, was created last year with a three-year mandate to advance AI, machine learning and associated technologies for U.S. national security.
2020-08-21 00:00:00 Read the full story…
Weighted Interest Score: 4.2309, Raw Interest Score: 1.7083,
Positive Sentiment: 0.2957, Negative Sentiment 0.1314
CloudQuant Thoughts : DARPA, key in the development of the Internet and Automated Cars getting involved in AI development. Eric Schmidt being involved. This is a major development for US maintaining its lead in AI.
BMW Boosting AI While Factory Lines are Paused from Pandemic
Pandemic-related shutdowns have enabled BMW to accelerate deployment of AI in its factories, with many projects focused on quality control. Here is a quality check with AI-based image recognition. (BMW)
With the Covid-19 pandemic forcing its factories to close, BMW AG is taking the opportunity to accelerate deployment of AI in its factories.
Matthias Schindler, head of AI innovation for BMW’s production systems group, has installed AI-powered quality control systems in many of the company’s 31 factories over the past three years, according to an account in the WSJPro. He had typically installed and tested new AI systems during planned work stoppages during holidays, but the pandemic-related shutdowns enabled work to happen in the factories without running into production.
The additional quality control checks are especially important to BMW given that cars are becoming more customizable, with different interior finishes, technical features, engine types and energy options.
2020-08-20 21:30:04+00:00 Read the full story…
Weighted Interest Score: 2.9791, Raw Interest Score: 1.1420,
Positive Sentiment: 0.2317, Negative Sentiment 0.2152
CloudQuant Thoughts : One does not think about the fact that these major manufacturers are completely closed down at the moment. It is not surprising that a company like BMW are utilizing this time to try out AI and ML on their production lines.
How 23-year-old Alexandr Wang built Scale AI into a $1 billion company in less than 3 years
Alexandr Wang is living the classic Silicon Valley success story. At just 23 he’s the CEO of a Scale AI, a four-year-old company he founded at age 19, dropping out of MIT to do so.
It hit unicorn status a year ago, when it had only been in business for three years. In those first three years, his company raised $123 million and reached a $1 billion valuation from a list of Silicon Valley’s who’s who. Angel investors include GitHub CEO Nat Friedman; OpenAI cofounder Greg Brockman; Instagram founders Kevin Systrom and Justin Kan; Quora cofounder and CEO Adam D’Angelo, Dropbox cofounder and CEO Drew Houston. His VCs backers include Peter Thiel at Founder’s Fund, Mike Volpi at Index Ventures, Dan Levine at Accel, and the list goes on.
Today, a year after raising $100 million at the billion-dollar valuation, he now employs about 180 people, he tells Business Insider and counts companies like Airbnb, SAP, Pinterest, Samsung, Doordash, Lyft and Toyota as customers, among others.
His company is building tools for AI developers trying to do for AI tech what cloud computing did for software development, building the infrastructure that makes it easier.
2020-08-23 00:00:00 Read the full story…
Weighted Interest Score: 2.4342, Raw Interest Score: 1.0616,
Positive Sentiment: 0.1840, Negative Sentiment 0.0849
Hedge Funds Using Artificial Intelligence Are Outperforming
Hedge funds utilising artificial intelligence capabilities have shown a competitive edge over investors that didn’t use AI, according to new research. The coronavirus pandemic has given partial proof of the effectiveness of the application of artificial intelligence as a predictive tool in fund management; reveals the latest issue of The Cerulli Edge―Global Edition.
An examination by Cerulli Associates of the assets under management (AUM) of various funds and net new flows of Europe-domiciled AI-enabled funds from 2013 to April this year reveals substantial AUM growth from 2016 to 2019. The aggregate return of AI-led hedge funds was almost three times higher than that of the overall hedge fund during this time: 33.9% compared to 12.1%.
Despite this, AI-powered hedge funds’ net new flows dropped somewhat last year, before dropping sharply mid-January and April. Nevertheless, Cerulli’s research tells that European AI-led active equity funds increased at a quicker rate than the other active equity funds from January to April this year and presented a less-pronounced slump in March
2020-08-19 Read the full story…
5 Reasons why you should Switch from Jupyter Notebook to Scripts
Using Scripts Helps me Realize the Drawbacks of Jupyter Notebook. Like most people, the first tool I used when started learning data science is Jupyter Notebook. Most of the online data science courses use Jupyter Notebook as a medium to teach. This makes sense because it is easier for beginners to start writing code in Jupyter Notebook’s cells than writing a script with classes and functions.
Like most people, the first tool I used when started learning data science is Jupyter Notebook. Most of the online data science courses use Jupyter Notebook as a medium to teach. This makes sense because it is easier for beginners to start writing code in Jupyter Notebook’s cells than writing a script with classes and functions. Another reason why Jupyter Notebook is such a common tool in data science is that Jupyter Notebook makes it easy to explore and plot the data. When we type ‘Shift + Enter’, we will immediately see the results of the code, which makes it easy for us to identify whether our code works or not.
However, I realized several fallbacks of Jupyter Notebook as I work with more data science projects:
- Unorganized: As my code gets bigger, it becomes increasingly difficult for me to keep track of what I write. No matter how many markdowns I use to separate the notebook into different sections, the disconnected cells make it difficult for me to concentrate on what the code does.
- Difficult to experiment: You may want to test with different methods of processing your data, choose different parameters for your machine learning algorithm to see if the accuracy increases. But every time you experiment with new methods, you need to rerun the entire notebook. This is time-consuming, especially when the processing procedure or the training takes a long time to run.
- Not ideal for reproducibility: If you want to use new data with a slightly different structure, it would be difficult to identify the source of error in your notebook.
- Difficult to debug: When you get an error in your code, it is difficult to know whether the reason for the error is the code or the change in data. If the error is in the code, which part of the code is causing the problem?
- Not ideal for production: Jupyter Notebook does not play very well with other tools. It is not easy to run the code from Jupyter Notebook while using other tools.
I knew there must be a better way to handle my code so I decided to give scripts a try. These are the benefits I found when using scripts:
2020-08-24 04:48:02.190000+00:00 Read the full story…
Weighted Interest Score: 2.9246, Raw Interest Score: 1.4327,
Positive Sentiment: 0.2439, Negative Sentiment 0.4268
AWS And Formula 1 Use Machine Learning To Find The Fastest Racer
“F1 and Amazon Machine Learning Solutions Lab took a full year to build the algorithm that led to the fastest driver.”
Formula 1 has been working with Amazon Web Services (AWS) to rank their racers. After a year of algorithmic heavy lifting, the results are out now. Ayrton Senna, the three-time world champion from Brazil came out on top, followed by the seven-time champion, Michael Schumacher with a time differential of +0.114 second. Whereas current World Champion Lewis Hamilton featured at 3rd position with a relative time of +0.275 seconds.
F1 is a brutal sport. The room for error at the top is almost non-existent. So, how and why was machine learning leveraged by F1 analysts?
2020-08-24 04:30:49+00:00 Read the full story…
Weighted Interest Score: 2.0000, Raw Interest Score: 1.0521,
Positive Sentiment: 0.2004, Negative Sentiment 0.1253
Process Flexibility Key to Consumer Lending in Post-COVID Market
Even before the pandemic, financial institutions had been investing in digital transformation and artificial intelligence (AI). Yet McKinsey finds that 70% of digital transformation programs fail to achieve their goals. In addition, successful deployment of AI is less than 10% in many organizations, according to the International Institute for Analytics.
Last year we partnered with Harvard Business Review Analytic Services to survey senior leaders to examine the most beneficial use cases for AI and the challenges preventing businesses from capitalizing on their AI investments. One key finding from the report was that operational decisions are an overlooked area and missed opportunity. Jim Marous, Co-Publisher of The Financial Brand, reiterated this finding in arguing that “digital transformation cannot occur without rethinking of the back-office processes” including how to streamline operations and integrate new data sources.
2020-08-18 00:02:11+00:00 Read the full story…
Weighted Interest Score: 2.1876, Raw Interest Score: 1.2177,
Positive Sentiment: 0.3228, Negative Sentiment 0.3668
Statistical analysis on a dataset you don’t understand
A sample analysis on a dataset where you know and understand nothing!
Recently, I took the opportunity to work on a competition held by Wells Fargo (Mindsumo). The dataset provided was just a bunch of numbers in various columns with no indication of what the data might be. I always thought that the analysis of data required some knowledge and understanding…
2020-08-24 02:37:43.147000+00:00 Read the full story…
Weighted Interest Score: 3.8187, Raw Interest Score: 1.3971,
Positive Sentiment: 0.2229, Negative Sentiment 0.1040
CDO Stature Rises, But Data Strategies Fall Short
The role of chief data officers (CDO) is expanding as companies look to unlock value in their vast stores of customer and other data, Still, many CDOs still face a misalignment between goals and priorities, a new vendor study funds.
The survey commissioned by enterprise cloud data management vendor Informatica and conducted by IDC found that 59 percent of CDOs report directly to their company’s chief executive, indicating that the “role of the CDO is becoming critical as one of the cornerstones of digital transformation,” the study stressed.
Meanwhile, 80 percent of CDOs’ key performance indicators are linked to business goals such as data privacy, operational efficiencies and revenues.
2020-08-19 00:00:00 Read the full story…
Weighted Interest Score: 3.7113, Raw Interest Score: 2.2487,
Positive Sentiment: 0.3066, Negative Sentiment 0.2385
The term ‘ethical AI’ is finally starting to mean something
Earlier this year, the independent research organisation of which I am the Director, London-based Ada Lovelace Institute, hosted a panel at the world’s largest AI conference, CogX, called The Ethics Panel to End All Ethics Panels. The title referenced both a tongue-in-cheek effort at self-promotion, and a very real need to put to bed the seemingly endless offering of panels, think-pieces, and government reports preoccupied with ruminating on the …
2020-08-23 00:00:00 Read the full story…
Weighted Interest Score: 3.6522, Raw Interest Score: 1.3278,
Positive Sentiment: 0.0843, Negative Sentiment 0.5199
Allen Institute for Artificial Intelligence’s new fund makes first investment, backing Panda AI
Panda AI announced an investment from the new fund out of Seattle’s Allen Institute for Artificial Intelligence (AI2).
GeekWire first reported about the stealthy Seattle startup back in June. The company spun out of AI2 and is the first to land cash from the organization’s pre-seed fund.
Panda AI raised a total of $3.3 million in a round led by PSL Ventures, with participation from AI2 and Ascend VC. Others including DocuSign co-founder Court Lorenzini and Smartsheet co-founder Eric Brown also invested.
2020-08-20 15:00:00+00:00 Read the full story…
Weighted Interest Score: 3.2568, Raw Interest Score: 1.6000,
Positive Sentiment: 0.2462, Negative Sentiment 0.0923
Guided Labeling Episode 4: From Exploration to Exploitation
One of the key challenges in using supervised machine learning for real world use cases is that most algorithms and models require a sample of data that is large enough to represent the actual reality your model needs to learn.
These data need to be labeled. These labels will be used as the target variable when your predictive model is trained. In this series we’ve been looking at different labeling techniques that improve the guided labeling process and save time and money.
What happened so far:
- Episode 1 introduced as to active learning sampling, bring the human back into the process to help guide the algorithm.
- Episode 2 discussed the label density approach, which follows the strategy that when labeling a dataset you want to label feature space that has a dense cluster of data points.
- Episode 3 moved on to the topic of model uncertainty as a rapid way of moving our decision boundary to the correct position using as few labels as possible and taking up as little time of our expensive human-in-the-loop expert.
2020-08-21 07:35:25+00:00 Read the full story…
Weighted Interest Score: 3.2150, Raw Interest Score: 1.6637,
Positive Sentiment: 0.1452, Negative Sentiment 0.2233
Amazon Launches Amzon Braket To Boost Quantum Computing Research
Amazon recently announced the launch of Amazon Braket which is a fully managed quantum computing service on AWS to boost research in this space. It aims to provide a user-friendly platform to get started with quantum computers and further explore the field with its potential applications.
Amazon Braket will provide an environment to design quantum algorithms, test them on simulated quantum computers, and run them on different types of quantum computing hardware. It will also provide managed Jupyter notebooks with pre-installed developer tools, sample algorithms, and tutorials to get started.
It will provide researchers access to quantum annealing hardware from D-Wave, and two types of gate-based quantum computers — ion-trap devices from IonQ and systems built on superconducting qubits from Rigetti, to carry their research.
2020-08-18 12:05:04+00:00 Read the full story…
Weighted Interest Score: 3.1430, Raw Interest Score: 0.9304,
Positive Sentiment: 0.1329, Negative Sentiment 0.0443
Using A Fantasy Game World To Boost AI Performance
Recently, Facebook AI Research (FAIR) built and deployed a role-playing fantasy game world to boost the performance of conversational AI models such as virtual assistants. The researchers presented a fully-realised system for improving an open-domain dialogue task by utilising a deployed game for lifelong learning.
The researchers built and deployed a role-playing game in which the human players converse with the learning agents that are situated in an open-domain fantasy world. They studied the ability of an open-domain1 dialogue model to learn from conversations with intrinsically motivated humans iteratively.
They stated, “In order to engage humans at scale, we build and deploy a (free to play) game with a purpose whereby human players role-play characters and converse with other characters (that are our learning models) situated within the game world.”
To maximise engagement, the researchers chose a fantasy game world. The system iterates between collecting data of human-model interactions, retraining updated models on the newly collected data and redeploying them. Simultaneously, it provides a natural metric to evaluate and compare models online using the continuation rate of players.
2020-08-24 07:30:00+00:00 Read the full story…
Weighted Interest Score: 3.0149, Raw Interest Score: 1.5963,
Positive Sentiment: 0.2425, Negative Sentiment 0.0202
GlobalTrading Podcast Episode 6: Data Science on the Buy Side
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Gary Collier, CTO of Man Group Alpha Technology, and Hinesh Kalian, Director of Data Science, Man Group, discuss the state of data science on the buy side, spanning its evolution, current challenges, and the future outlook. The podcast is moderated by Global Trading Editor Terry Flanagan.
2020-08-18 14:34:23+00:00 Read the full story…
Weighted Interest Score: 6.1489, Raw Interest Score: 1.9417,
Positive Sentiment: 0.0000, Negative Sentiment 0.3236
Pachyderm Gains Microsoft Funding, Launches Hub
A startup launched as a Hadoop alternative in the form of a container-based big data platform continues to attract investors to its open source data science framework.
Pachyderm Inc. said this week its $16 million Series B fund was led by M12, Microsoft’s (NASDAQ: MSFT) venture fund. New investors include Decibel Ventures, which is backed by Cisco Systems (NASDAQ: CSCO), and returning investors, among them, Benchmark and Y Combinator.
2020-08-19 00:00:00 Read the full story…
Weighted Interest Score: 4.4996, Raw Interest Score: 2.1368,
Positive Sentiment: 0.0777, Negative Sentiment 0.0389
iKala, an AI-based customer engagement platform, raises $17 million to expand in Southeast Asia
iKala, a Taiwanese startup that offers an artificial intelligence-based customer acquisition and engagement platform, will expand into new Southeast Asian markets after raising a $17 million Series B. The round was led by Wistron Digital Technology Holding Company, the investment arm of the electronics manufacturer, with participation from returning investors Hotung Investment Holdings Limited and Pacific Venture Partners. It brings iKala’s total raised so far to $30.3 million.
The new funding will be used to launch in Indonesia and Malaysia, and expand in markets where iKala already operates, including Singapore, Thailand, Hong Kong, the Philippines, Vietnam and Japan. Wistron Digital Technology Holding Company, which also offers big data analytics, will serve as a strategic investor, and this also marks the Taiwanese firm’s entry into Southeast Asia.
2020-08-19 Read the full story…
Fundamentals of Machine Learning Enabled Analytics
The famous theoretical physicist Stephen Hawking said, “It’s tempting to dismiss the notion of highly intelligent machines as mere science fiction.”
Artificial intelligence (AI), the game-changer technology of the global business world, comprises three distinct sub-disciplines: machine learning (ML), natural language processing (NLP), and cognitive computing. Automated solutions in business analytics use all these sub-technologies, but in varying degrees. Most advanced analytics platforms have incorporated ML or deep learning (DL) techniques to remain competitive in the market.
According to Gartner, 40 percent of all new enterprise applications will include AI technologies by 2021. On the other hand, organizations are flooded with data; the current challenge is extracting competitive intelligence from that “deluge of data.” Businesses that plan on surviving the digital tsunami (big data and IoT), have all put a definite business strategy in place, which connects data, analytics, and AI across the operative landscape.
2020-08-18 Read the full story…
Banks Must Bet Big On AI And Blockchain: Prasanna Lohar, Head of Innovation – DCB Bank
Prasanna Lohar currently works as Head – Innovation & Technical Architecture at DCB Bank. As a part of DCB’s digital transformation, he is firmly focused on innovative customer servicing, technical architecture implementation, and adoption of emerging technologies for banking.
In this interview, Prasanna talks about the fast-changing disruptions in the banking sector brought about by changing customer needs and fintech. Further, he sheds light on how his bank uses data analytics for strategic innovation and the use case of blockchain for solving the NPA crisis and improving India’s credit system. Here are the excerpts from the interaction:
2020-08-18 Read the full story…
Is your BI team AI ready? Enter AutoML 2.0 (Sponsored dotdata)
The notion of using data to predict future outcomes is far from new. Even highly technical products that performed “predictive analytics” analysis have already been available to enterprise organizations for many years. The notion of developing and deploying custom-built predictive solutions, however, have, for the most part, been the exclusive domain of Fortune 500 companies.
The rarity of predictive analytics in the enterprise is mostly due to the technical complexity needed to create, train, and deploy the complex AI and Machine Learning (ML) models required to successfully develop predictive solutions. Over the past few years, the world of AI and ML development has seen rapid change. One of the most critical areas of progress has been the automation of the training of ML models.
2020-08-19 Read the full story…
Global Artificial Intelligence Conference – 2020 Sep 16th – 18th – Seattle – WA
Global Big Data Conference’s vendor agnostic Global Artificial Intelligence Conference is held on Sep 16th – 18th 2020 on all industry verticals(Finance, Retail/E-Commerce/M-Commerce, Healthcare/Pharma/BioTech, Energy, Education, Insurance, Manufacturing, Telco, Auto, Hi-Tech, Media, Agriculture, Chemical, Government, Transportation etc.. ). It will be the largest vendor agnostic conference in AI space. The Conference allows thought leaders & practitioners to discuss AI through effective use of various techniques.
2020-09-16 00:00:00 Read the full story…
Weighted Interest Score: 4.4335, Raw Interest Score: 2.1565,
Positive Sentiment: 0.3697, Negative Sentiment 0.0000
The 3 key attributes you need to win a spot at the world’s first AI-focused university, according to its top academic
The world’s first university dedicated to the study of AI is preparing to welcome its first cohort of students in Abu Dhabi.
The Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) is part of Abu Dhabi’s wider attempts to focus its economy on knowledge and skills.
Sir Michael Brady, a pioneer of AI technology and interim president of MBZUAI, told Business Insider the three things prospective students will need to win a place at the university.
Visit Business Insider’s homepage for more stories.
The world’s first university dedicated to the study of AI is peparin…
2020-08-24 00:00:00 Read the full story…
Weighted Interest Score: 3.9269, Raw Interest Score: 2.1017,
Positive Sentiment: 0.1356, Negative Sentiment 0.0678
Here’s the pitch deck deep tech firm Apheris used to persuade Twitter chair and ex-Google CFO Patrick Pichette to invest
Berlin-based deep tech startup Apheris raised funding from institutional investors and angel investors, including Twitter chair Patrick Pichette, in a $3 million seed fundraising round.
Apheris helps private companies navigate the complexities of local data privacy laws, allowing them to extract insights from datasets through the use of AI technology.
We got an exclusive look at the pitch deck Apheris used to bring investors on board.
2020-08-24 00:00:00 Read the full story…
Weighted Interest Score: 3.7463, Raw Interest Score: 2.0979,
Positive Sentiment: 0.2498, Negative Sentiment 0.1499
Motive found in CC Capital bid for MLC Wealth
C Capital has reunited with financial services technology specialist Motive Partners – the New York and London-based firm that joined CC Capital and a bunch of other private equity players to acquire data analytics business Dun & Bradstreet for $US6.9 billion last year.
Motive is understood to be willing to provide expertise and capital as part of the CC Capital bid for MLC Wealth. and seek to repeat the successful combination from Dun & Bradstreet. The analytics firm listed in the United States in July and now has a $US10.8 billion ($15 billion) market capitalisation.
2020-08-24 00:00:00 Read the full story…
Weighted Interest Score: 3.6981, Raw Interest Score: 1.9742,
Positive Sentiment: 0.0759, Negative Sentiment 0.0000
Can Deep Learning Maintain Online Trading Profitability Right Now?
Deep learning technology has rattled the global financial industry in both positive and negative ways. On the one hand, deep learning technology has considerably improved market efficiency. Tomiwa, a big data author and expert, claims to have beaten the stock market average over the past ten years with a program that he developed with Python. The same kind of program could be used by Forex or derivative traders.
One of the biggest downsides, though, is that it has giving larger institutional traders with deep pockets an even stronger advantage. Robotrading has been a concern in the Investing community for a long time. Deep learning has only widened the chasm of opportunities between large investors and everyday speculators.
Some of these concerns have become even more pronounced during the COVID-19 crisis. The good news is that regular investors can still benefit from deep learning technology. They just need to know how to utilize it effectively.
2020-08-18 18:00:40+00:00 Read the full story…
Weighted Interest Score: 3.5954, Raw Interest Score: 2.1039,
Positive Sentiment: 0.5399, Negative Sentiment 0.3538
Commonwealth collaborates with JP Morgan on emerging tech
Commonwealth and JP Morgan Chase are collaborating on a two-year initiative focusing on emerging technology.
The project aims to address the challenges and opportunities that emerging tech presents to lower- and moderate-income people’s financial lives.
As part of the two-year initiative, Commonwealth will conduct research, understand and document the financial landscape for financially vulnerable people. It will examine usage patterns of emerging tech with a focus on how they garner trust.
It will show how emerging technologies can address acute financial challenges faced by financially vulnerable people during COVID-19, and on the path to recovery.
Commonwealth data shows 43% of lower income workers do not have a savings account.
2020-08-20 07:00:45+00:00 Read the full story…
Weighted Interest Score: 3.5031, Raw Interest Score: 1.5173,
Positive Sentiment: 0.2529, Negative Sentiment 0.2890
AI at the Far Edge
The concept of “edge computing” has been around since the late 90s, and typically refers to systems that process data where it is collected instead of having to both store and push it to a centralized location for off-line processing. The aim is to move computation away from the data center in order to faciliate real-time analytics and reduce network and response latency. But some applications, particularly those that leverage deep learning, have been historically very difficult to deploy at the edge where power and compute are typically extremely limited. The problem has become particularly accute over the past few years as recent breakthroughs in deep learning have featured networks with a lot more depth and complexity, and thus require greater compute from the platforms they run on. But recent developments in the embedded hardware space have bridged that gap to a certain extent and enable AI to run fully on the edge, ushering a whole new wave of applications. And new data scientists and machine learning engineers entering the field are going to need to be prepared on how to leverage these platforms to build the next generation of truly “smart” devices.
2020-08-21 10:16:25-05:00 Read the full story…
Weighted Interest Score: 3.1141, Raw Interest Score: 1.9809,
Positive Sentiment: 0.1631, Negative Sentiment 0.0932
IIT Madras Invites Applications For Post-Doctoral Fellowship In Data Science & AI
The Robert Bosch Centre for Data Science and Artificial Intelligence (RBC DSAI) at IIT Madras has invited applications for its Post-Doctoral Fellowship. It is open to candidates across the country with PhD Degrees in Research Topics related to Data Science, Artificial Intelligence or allied application domains.
The areas of research include Deep Learning, Network Analytics, Theoretical Machine Learning, Reinforcement Learning and Multi-armed Bandits, Natural Lang…
2020-08-17 08:55:14+00:00 Read the full story…
Weighted Interest Score: 3.0471, Raw Interest Score: 1.8661,
Positive Sentiment: 0.1647, Negative Sentiment 0.0549
An Extensive Step By Step Guide for Data Preparation
A go-to resource for preparing your data for data science. Before we get into this, I want to make it clear that there is no rigid process when it comes to data preparation. How you prepare one set of data will most likely be different from how you prepare another set of data. Therefore this guide aims to provide an overarching guide that you can refer to when preparing any particular set of data.
Data preparation is the step after data collection in the machine learning life cycle and it’s the process of cleaning and transforming the raw data you collected. By doing so, you’ll have a much easier time when it comes to analyzing and modeling your data. There are three main parts to data preparation that I’ll go over in this article:
- Exploratory Data Analysis (EDA)
- Data preprocessing
- Data splitting
2020-08-24 00:19:00.838000+00:00 Read the full story…
Weighted Interest Score: 2.7988, Raw Interest Score: 1.5144,
Positive Sentiment: 0.0528, Negative Sentiment 0.1761
Can Machine Learning Models Accurately Predict The Stock Market?
Artificial intelligence is viewed as the Holy Grail of technology. It’s being investigated as a way of solving many of the complex problems that face mankind. What makes artificial intelligence attractive is that it combines unbelievably fast computing power with an intuitiveness that was previously only available from human involvement.
Artificial intelligence is being used in the financial markets. Many believe that soon artificial intelligence will crack at the proverbial code of the markets by taking advantage of big data and machine learning.
There are online trading platforms that allow users to take advantage of machine learning and artificial intelligence. Artificial intelligence has not reached the point where it has unlocked the secrets of making money in the market. What it is doing currently is giving investors a systemic edge. Computers are getting better at recognizing when risks should be taken and the amount of risk to take.
2020-08-16 21:41:12+00:00 Read the full story…
Weighted Interest Score: 2.6061, Raw Interest Score: 1.8701,
Positive Sentiment: 0.2843, Negative Sentiment 0.2244
The Top Trends in Data Management for 2021 (Registration)
From the rise of hybrid and multicloud architectures, to the impact of machine learning and automation, the business of data management is constantly evolving with new technologies, strategies, challenges and opportunities. The demand for fast, wide-range access to information is growing. At the same time, the need to effectively integrate, govern, protect and analyze data is also intensifying. All the while, data environments are increasing in size and complexity — traversing relational and non-relational databases, transactional and analytical systems, and on-premises and cloud sites.
Join us for a special expert panel on December 10th to dive into the key technologies and strategies to keep on your radar for 2021.
2020-12-10 00:00:00 Read the full story…
Weighted Interest Score: 2.5974, Raw Interest Score: 1.6729,
Positive Sentiment: 0.0929, Negative Sentiment 0.0929
GBG leads $7 million investment in alternative scoring specialist CredoLab
Identity data specialist GBG has led a $7 million funding round in Singapore’s CredoLab. CredoLab develops digital risk scorecards for banks, lenders, e-commerce, travel, ride hailing, e-wallets, insurance and retail companies by using privacy-consented and permissioned, smartphone and web behavioural data.
Built on over 22 million credit applications across more than 70 lending partners, CredoLab’s artificial intelligence based algorithm crunches millions of features to find the most predictive micro-behavioural patterns, before converting them into risk scores. Established in 2016, the firm has so far approved over $2 billion in loans to date, many of which have been applied to traditional hard-to-categorise ‘thin file’ credit applicants.
2020-08-20 14:27:00 Read the full story…
Weighted Interest Score: 2.5641, Raw Interest Score: 1.7642,
Positive Sentiment: 0.0802, Negative Sentiment 0.0000
Modern Data Warehousing: Enterprise Must-Haves (Registation)
To fit into modern analytics ecosystems, legacy data warehouses must evolve – both architecturally and technologically – to deliver the agility, scalability and flexibility that business need to thrive in today’s data-driven economy. Alongside new architectural approaches, a variety of technologies have emerged as key ingredients of modern data warehousing, from data virtualization and cloud services, to Hadoop and Spark, and machine learning and automation. To educate IT decision makers and data warehousing professionals about the must-have capabilities for modern data warehousing today – how they work and how best to use them – DBTA is hosting a special roundtable webinar on November 19th.
2020-11-19 00:00:00 Read the full story…
Weighted Interest Score: 2.5448, Raw Interest Score: 1.6053,
Positive Sentiment: 0.0944, Negative Sentiment 0.0000
A Model for Creating a Data-Driven Culture
Over the past decade, firms have taken the plunge to become data driven. They have amassed data, invested in technologies, and paid handsomely for analytical talent. Yet for many, a strong, data-driven culture remains elusive and data is not universally used for decision making. Too often, this plunge has not yet paid off.
Why is it so hard?
In his recent Harvard Business Review article, “10 Steps to Creating a Data-Driven Culture,” David Waller writes, “The business obstacles to creating data-based businesses aren’t technical; they’re cultural.”
On August 19, 2020, David Waller—head of data science and analytics for Oliver Wyman Labs—will lead a live, interactive HBR webinar. He will discuss challenges companies face in shifting to a data-driven mindset and will share 10 data commandments to create and sustain a culture with data at its core.
Among the 10 commandments Waller will discuss are:
- A data-driven culture starts at the (very) top
- Don’t pigeonhole your data scientists
- Make proofs of concept simple and robust, not fancy and brittle
2020-08-19 16:00:00+00:00 Read the full story…
Weighted Interest Score: 2.4885, Raw Interest Score: 1.2443,
Positive Sentiment: 0.0655, Negative Sentiment 0.1310
What can financial services learn from Big Tech? Adopt a similar approach to data architecture
Many tech stocks continue their meteoric rise, despite the worsening economic downturn. On Wednesday this week Apple became Wall Street’s first $2tn company. In my last Finextra article I mentioned that Apple, along with the other big four, Microsoft, Amazon, Google and Facebook, now make up a quarter of the S&P 500. We are seeing new investment strategies (and acronyms) emerging with this tech boom, such as ANTMAN – referring to taking big bets on Amazon, Netflix, Tesla, Microsoft, Apple, and Nvidia. An investment here would have returned 76% since the global pandemic was declared on January 30th.
So, what can financial services firms, which now account for just 10% of the S&P 500, learn from the successful tech sector? One common factor across the tech companies is their leading edge data architectures, enabling them to make use of data in real-time, offering exceptional customer experiences, at massive scale.
2020-08-24 10:54:44 Read the full story…
Weighted Interest Score: 2.4728, Raw Interest Score: 1.5609,
Positive Sentiment: 0.1754, Negative Sentiment 0.1754
iFarm Raises $4 million to automate urban farming with AI and drones
iFarm has raised $4 million to expand its automated system that uses AI and drones to grow fruits and vegetables in enclosed spaces. Gagarin Capital led the round of funding, which included investment from Matrix Capital, Impulse VC, IMI.VC, and some business angels.
The Finnish startup has developed a vertical agricultural system called iFarm Growtune. By growing food closer to consumers and in spaces where conditions can be carefully controlled, iFarm promises to produce food that is fresher while reducing environmental impact.
As companies rethink logistics and the environment in the wake of the pandemic, self-contained urban farms hold growing appeal.
“The main advantage of indoor farms is that you can be growing all year round, wherever you are,” said iFarm cofounder and CEO Max Chizhov. “And you don’t need a special technologist or agronomist who knows how to use software or grow stuff.”
2020-08-20 00:00:00 Read the full story…
Weighted Interest Score: 2.4215, Raw Interest Score: 1.1738,
Positive Sentiment: 0.0379, Negative Sentiment 0.0000
Palantir reportedly lost $580 million in 2019 and plans lockup after direct listing
Data analytics software company Palantir Technologies lost $580 million in 2019, according to reports from The New York Times and TechCrunch, which had access to financial documents sent to investors earlier this week.
The New York Times said its 2019 loss was on a par with 2018, even though it earned 25% more for a total of $724.5 million in revenue for the year. It had more than $1 billion in expenses, accor…
2020-08-21 00:00:00 Read the full story…
Weighted Interest Score: 2.3758, Raw Interest Score: 1.6559,
Positive Sentiment: 0.0000, Negative Sentiment 0.1440
Incumbent banks favour inhouse AI development
Large incumbent banks are more likely to build artificial intelligence (AI) capabilities inhouse rather than outsource them, according to Kevin Levitt, global industry business development at NVIDIA during an online MoneyNext Summit panel today.Lewitt referenced an AI in banking survey currently being fielded by NVIDIA.“The early results are saying that just shy of two-thirds, about 57 percent, are developing their own [AI capabilities], 22 percent are co-developing…
2020-08-18 00:00:00 Read the full story…
Weighted Interest Score: 2.3711, Raw Interest Score: 1.0996,
Positive Sentiment: 0.2377, Negative Sentiment 0.0000
Make it Automatic: Tuning SQL with AI
The database administrator (DBA) is typically tasked with making applications run more efficiently in order to meet service level agreements (SLAs) or just to ensure optimum user experience. From the users’ perspective, this means faster execution times and quicker application response times. However, from a database systems management perspective, the DBA must pay close attention to overall workload resource usage, both in real-time and long-ter…
2020-08-20 00:00:00 Read the full story…
Weighted Interest Score: 2.2681, Raw Interest Score: 1.2676,
Positive Sentiment: 0.3422, Negative Sentiment 0.2662
We need to democratize data literacy
We’ve all heard the maxim that data is king. Since the early 2000s, the power of data has ballooned unchecked as our economies hurtled towards digitization. Brands and corporations that recognized the opportunity have amassed untold wealth, while legislators are still scrambling to retrofit rules to govern its use.
Yet although the tussles between governments and Big Tech dominate the headlines, we’re overlooking a wider societal shift in which data is playing the starring role. As some market players deepen their understanding and increase their power, those who don’t have a handle on their own data fall further behind. As “traditional” businesses disintegrate and digital tightens its grip, we’re at risk of creating a new hierarchy of power: where the data literates reign over the data illiterates.
Being data illiterate doesn’t mean you don’t have access to data. Few companies these days operate in a data free zone (the mass panic over GDPR is testament to that). Rather, data illiteracy results from a lack of the skills, time or resources needed to properly understand and utilize insights. As data illiterates fall further behind, their economic potential diminishes. For those desperate to catch up, many end up outsourcing their data needs — thereby funneling more power to the already powerful and pushing comprehension of their own data further out of reach.
2020-08-22 00:00:00 Read the full story…
Weighted Interest Score: 2.2056, Raw Interest Score: 1.2603,
Positive Sentiment: 0.3676, Negative Sentiment 0.2626
ProBeat: Release your data sets to the AI research community and reap the benefits
This week we featured how Duolingo uses AI in every part of its app, including Stories, Smart Tips, podcasts, reports, and even notifications. The story is based on interviews with CEO Luis von Ahn and research director Burr Settles, who joined as the company’s first AI hire in 2013 (Duolingo was founded in 2012). While that story covers the AI in Duolingo specifically, which I think is relevant to any startup looking to invest in AI early, I wanted to publish the tail end of my interview with Burr for its even broader insights.
But first, some context from the top of our discussion. “We approach AI projects in three kinds of ways,” Settles explained. “AI to help facilitate building high quality content in our processes. AI to create more engaging and exciting to keep people coming back. And then AI to kind of knowledge model and then personalize the experiences. So we’ve got projects going on in all three of those areas.”
The below transcript will make more sense if you read the Duolingo story first. One observation: How Settles describes Duolingo releasing its data sets reminds me of the early days of Mozilla building its browser in the open and how the ensuing open source revolution affected software development.
2020-08-21 00:00:00 Read the full story…
Weighted Interest Score: 2.1353, Raw Interest Score: 0.9714,
Positive Sentiment: 0.2220, Negative Sentiment 0.0833
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