AI & Machine Learning News. 09, March 2020
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?
Women in Data Science (WiDS) Stanford Conference Replay (8 hours)
On March 2, 2020, 400+ people gathered at Stanford University and thousands more gathered across the globe for the 5th annual Women in Data Science (WiDS) Conference.
Watch the recorded livestream, or you can watch individual WiDS Stanford speaker presentations on YouTube.
- WiDS Welcome | Margot Gerritsen, Karen Matthys and Judy Logan
- Opening Address | Persis Drell
- Machine Learning: A New Approach to Drug Discovery | Daphne Koller
- Why a World with AI Needs More EQ | Tsu-Jae King Liu
- Interpretability For Everyone | Been Kim
- How Data Science Can Unlock Teaching & Learning at Scale | Emily Glassberg Sands
- Building Water Security From the Bottom Up by Leveraging Big Data | Newsha Ajami
- Creating Global Economic Opportunity with Responsible Data Science | Ya Xu
- Ethics Panel
- Don’t Look. See! Are We Blinded by Data (Visualization)? | Fanny Chevalier
- Know Thyself: Introspective Personal Data Mining | Talithia Williams
- Data Science in a Cloud World: What Every Data Scientist Needs to Know | Nhung Ho
- Polyglot AI: The Role of Natural Language Processing (NLP) | Rama Akkiraju
- Career Panel
2020-03-02 00:00:00 Read the full story…
Weighted Interest Score: 3.0048, Raw Interest Score: 1.8630,
Positive Sentiment: 0.4207, Negative Sentiment 0.2404
CloudQuant Thoughts : We gave it a shout out last week and here is the recap, you can watch at your leisure.
Data science and AI are a mess… and your startup might be making it worse – Cassie Kozyrkov
Data science has been called “the sexiest job of the 21st century” but sometimes I wonder whether we’re off by a century here. Is the world ready for us? I’ve looked into this question before, but the tools for data science issue warrants more discussion. The tools available to data scientists put a cap on their effectiveness, so it would be great to see toolmakers paying more attention to their needs. Instead, it feels like the tools are made for buzzwords instead of people.
This article was inspired by my friend Clemens Mewald—one of the best product managers I’ve had the honor of working with—who wrote a piece titled “Your Deep-Learning-Tools-for-Enterprises Startup Will Fail” …which I read with the same emotion I feel when my toys are about to be yanked away. It feels as though the tools are made for buzzwords instead of people.
On the one hand, he’s right: if you go about making ML/AI developer tools like all the rest of ’em, your startup will probably go under. On the other hand, I don’t want startup folk to run away screaming.
I say this purely selfishly, as a data scientist pleading on behalf of her people. I recognize my privilege of working in an environment that suffers relatively little from the problems I’m about to bring up, so I want you to know that these words are inspired by my experience of how things used to be (even here at Google!) and by the stories you share with me daily. Let me lend you my voice.
2020-03-07 14:02:38.722000+00:00 Read the full story…
Weighted Interest Score: 2.1915, Raw Interest Score: 1.2262,
Positive Sentiment: 0.3783, Negative Sentiment 0.2087
CloudQuant Thoughts : Cassie’s posts are always entertaining and this one is no different.
New Open Source App: Data Science Education
The principles underpinning open source software development that are transforming the digital economy are now being extended to new sectors such as education, where proponents hope to leverage the collaborative approach to advance the teaching of data science.
An open source project shepherded by the Linux Foundation aims to accelerate data science curricula while benefitting from the contributions of students and teachers. OpenDS4All is funded by IBM (NYSE: IBM) and is being developed by the University of Pennsylvania. The effort would give educators free access to information needed to develop data science coursework. In return, successful approaches would be folded back into what project promoters call “constantly evolving and improving” curricula.
2020-03-04 00:00:00 Read the full story…
Weighted Interest Score: 3.2821, Raw Interest Score: 1.9335,
Positive Sentiment: 0.1813, Negative Sentiment 0.0302
CloudQuant Thoughts : Don’t you hate when you discover a white paper that speaks exactly to your current interest but you cannot source the data or reproduce the results. The Data Science Foundation should help resolve this. But in the environment of Stock Markets and Trading, data is like gold and access is restricted. So it would be useful to find white papers on important trading and alternative data trends that actually worked out of the box, contained all the data. CloudQuant create their own white papers to provide this exact service over at our data catalog.
Babylon to train its health chatbot to recognise coronavirus symptoms
Digital doctor app Babylon Health is searching for ways to train its artificial intelligence-driven chatbot to detect coronavirus symptoms as the number of cases of the deadly Covid-19 strain rises in the UK.
The British start-up, which has been lauded by Health Secretary Matt Hancock, operates an AI chatbot in its GP at Hand app. it claims it can “identify likely causes” of an illness once patients input symptoms.
Dr Keith Grimes, clinical AI director at Babylon, cautioned that changes to the AI would be difficult due to the lack of “robust data” needed to train the underlying algorithms.
Babylon is currently urging patients not to use its AI symptom checker if they think they have been infected by the new coronavirus strain….
2020-03-08 00:00:00 Read the full story…
Weighted Interest Score: 3.8017, Raw Interest Score: 1.3223,
Positive Sentiment: 0.1653, Negative Sentiment 0.4959
CloudQuant Thoughts : I love that final line in the article preview!!!
Oracle Rolls Out Data Science and Machine Learning Services
Oracle recently announced the availability of the Oracle Cloud Data Science Platform with Oracle Cloud Infrastructure Data Science at the core, helping enterprises to collaboratively build, train, manage, and deploy machine learning models.
The goal with Oracle’s Cloud Infrastructure Data Science is to improve the collaboration and effectiveness of data science teams with capabilities such as shared projects, model catalogs, team security policies, reproducibility, and auditability. Oracle Cloud Infrastructure Data Science automatically selects the most optimal training datasets through AutoML algorithm selection and tuning, model evaluation and model explanation.
2020-03-04 00:00:00 Read the full story…
Weighted Interest Score: 5.4091, Raw Interest Score: 2.7588,
Positive Sentiment: 0.3876, Negative Sentiment 0.0912
AI Spawning New Products in Investment Business
Liquidnet recently announced plans to launch a data service for money management firms that uses AI to search for hidden information that can affect investment decisions, according to an account in WSJPro. Liquidnet operates a dark pool, a private financial forum for buying and selling securities, that lets investors stay hidden until a trade is executed.
The new service, called Investment Analytics, will use AI to analyze financial reports, earnings calls, news articles, and other sources. The company appointed Vicky Sanders, a co-founder of RSRCHXchange, which Liquidnet acquired last year, as the global head of Investment Analytics. RSRCHXchange was a financial tech company that provided asset management firms with a repository of reports based in the cloud.
The plan is to combine the services with two other recent Liquidnet acquisitions, OTAS Technologies and Prattle, to use AI for a new investment product. OTAS uses AI to analyze data on liquidity, volumes, and spreads. Prattle uses natural language processing and machine learning to analyze publicly-available content.
2020-03-05 22:30:34+00:00 Read the full story…
Weighted Interest Score: 4.5622, Raw Interest Score: 1.8949,
Positive Sentiment: 0.2369, Negative Sentiment 0.0646
TradeTech 2020 unites Europe’s top equity trading leaders including regulators, sell side, trading platforms, technology partners and over 500 senior buy side. This is your unique opportunity to join the leading buy side in using data and technology to thrive in the post-MiFID II liquidity landscape.
Brand new for our 20th and best-ever TradeTech: buy side are invited to join our exclusive data science day. Sign-up for free today – very limited availability.
TradeTech 2020 has evolved:
- Connect with 1200+ equity trading and tech leaders representing the world’s leading financial institutions such as Blackrock, UBS Asset Management, Generali, JP Morgan Asset Management and Vanguard Asset Management amongst other influential players in this exciting field.
- Hear 500+ buy-side heads of trading and learn how to adopt smarter approaches to access liquidity, build the best execution strategies, and implement Machine Learning techniques and smart data analysis to automate trading.
- Whether you work in Data Science, Trading or Technology/IT, Tradetech 2020 has tailored streams to provide you with the latest updates on regulation, market structure developments and cutting-edge execution strategies.
2020-03-09 00:00:00 Read the full story…
Weighted Interest Score: 4.5417, Raw Interest Score: 2.1066,
Positive Sentiment: 0.3304, Negative Sentiment 0.0000
Syncsort Partners with Databricks to Support Cloud Initiatives
Syncsort is partnering Databricks to support cloud initiatives for critical mainframe and IBM i data, enabling enterprises to leverage Syncsort Connect products to access, transform, and deliver mainframe data to Delta Lake.
Organizations rely on Databricks to process massive amounts of data in the cloud and power AI, machine learning and business insights. Syncsort Connect features a design once, deploy anywhere architecture that provides a graphical interface to deploy mainframe to cloud data transformation pipelines.
Integration with Syncsort Connect products enables the combination of the Databricks platform with Syncsort’s unrivaled ability to integrate previously inaccessible mainframe and IBM i data for analytics and data science.
2020-03-09 00:00:00 Read the full story…
Weighted Interest Score: 4.3342, Raw Interest Score: 2.1038,
Positive Sentiment: 0.1791, Negative Sentiment 0.2686
AI in Human Resources: 5 Trends in 2020 and Beyond
Many new and emerging technologies are adding value to human resources. This explains their high adoption rate. One of these technological marvels is artificial intelligence or AI. Is there a place for AI in HR? Apparently, there is. More than 66% of CEOs in IBM’s survey report they believe cognitive computing has an important role in HR. It’s definitely worth exploring the AI trends in HR in 2020 and beyond.
- HR Task Automation
- AI-Powered Chatbots As New Recruiters
- Smart LMS System As New HR Training Software Solution
- AI Facilitates Data-Driven Decisions
- AI-Powered HR Helpdesk
2020-03-09 00:00:00 Read the full story…
Weighted Interest Score: 4.1653, Raw Interest Score: 1.7968,
Positive Sentiment: 0.3023, Negative Sentiment 0.1175
Dataiku Announces Global 2020 EGG Conference Series, Expanding to 8 Cities
Today, Dataiku, one of the world’s leading Enterprise AI and machine learning platforms, announced the lineup for its 2020 EGG Conferences, with events planned in eight cities worldwide. EGG, The Human-Centered AI Conference, is a series of one-day gatherings focused on issues at the forefront of data science, machine learning, and AI, exploring real-life Enterprise AI use cases and how to create organizational change with scalable AI systems that enhance – not replace – humans.
Since 2017, EGG has attracted more than 5,000 data leaders at the world’s first conference focused not just on what AI can do, but, practically, how companies can get there. As human-centered AI as a concept continues to gain traction globally, the conference is expanding to new and exciting hubs for AI: Montreal, Frankfurt and Sydney.
The 2020 EGG Series includes:
- New York City – June 11
- London – June 23
- Montreal – Sept. 24
- Sydney – Oct. 12
- San Francisco – Oct. 21
- Paris – Nov. 3
- Amsterdam – Nov. 10
- Frankfurt – Nov. 24
2020-03-06 00:00:00 Read the full story…
Weighted Interest Score: 4.1015, Raw Interest Score: 1.5914,
Positive Sentiment: 0.2850, Negative Sentiment 0.1188
Billionaire investor Steve Cohen is reportedly raising money for a new fund focused on the AI space
Billionaire investor Steve Cohen is stepping outside the hedge fund industry and creating a private-markets fund, The Wall Street Journal reported Friday.
The fund, named Point72 Hyperscale, will act as a hybrid between a venture capital firm and a private-equity fund and focus on companies in the artificial intelligence space, sources told The Journal. Hyperscale aims to raise $500 million to $900 million in 2020, with Cohen as its anchor investor. The private-markets fund is Cohen’s first investor offering outside the hedge fund space. He previously founded SAC Capital Advisors and Point72 Asset Management.
2020-03-10 00:00:00 Read the full story…
Weighted Interest Score: 3.9870, Raw Interest Score: 1.9131,
Positive Sentiment: 0.0000, Negative Sentiment 0.1297
AI is Being Used to Discover New Antibiotics and Genes Linked to Disease
New types of antibiotics are being developed using an AI machine-learning approach that scans a pool of more than 100 million molecules, including one that works against strains of bacteria previously considered untreatable, according to a recent account in Nature.
The antibiotic, called halicin (named after the HAL 9000 computer from 2001: A Space Odyssey), is believed to be the first discovered with AI. While AI had been applied to parts of the antibiotic-discovery process, the researchers said this was the first time AI had helped to identify a completely new kind of antibiotic from scratch. Led by synthetic biologist Jim Collins at MIT, the paper is published in Cell.
New drugs are needed to fight growing bacterial resistance to antibiotics worldwide, resulting in infections that could kill 10 million people per year by 2050. New drug development has slowed over the past several decades. “People keep finding the same molecules over and over,” stated Collins. “We need novel chemistries with novel mechanisms of action.”
2020-03-05 22:30:19+00:00 Read the full story…
Weighted Interest Score: 3.7242, Raw Interest Score: 1.7188,
Positive Sentiment: 0.1023, Negative Sentiment 0.1023
Kubernetes Gets an Automated ML Workflow
A stable version of an automation tool released this week aims to make life easier machine learning developers training and scaling models, then deploying ML workloads atop Kubernetes clusters.
Roughly two years after its open source release, Kubeflow 1.0 leverages the de facto standard cluster orchestrator to aid data scientists and ML developers in tapping cloud resources to run those workloads in production. Among the stable workflow applications released on Monday (March 2) are a central dashboard, Jupyter notebook controller and web application along with TensorFlow and PyTorch operators for distributed training.
2020-03-03 00:00:00 Read the full story…
Weighted Interest Score: 3.6762, Raw Interest Score: 2.0732,
Positive Sentiment: 0.1944, Negative Sentiment 0.0324
3 important trends in AI/ML you might be missing
According to a Gartner survey, 48% of global CIOs will deploy AI by the end of 2020. However, despite all the optimism around AI and ML, I continue to be a little skeptical. In the near future, I don’t foresee any real inventions that will lead to seismic shifts in productivity and the standard of living. Businesses waiting for major disruption in the AI/ML landscape will miss the smaller developments.
Here are some trends that may be going unnoticed at the moment but will have big long-term impacts:
- Specialty hardware and cloud providers are changing the landscape
- Innovative solutions are emerging for, and around, privacy
- Robust model deployment is becoming mission critical
2020-03-08 00:00:00 Read the full story…
Weighted Interest Score: 3.5708, Raw Interest Score: 1.6596,
Positive Sentiment: 0.2202, Negative Sentiment 0.3895
7 Ways To Kill Your Data Scientist Career (Without Knowing It)
In the era where data is the most valuable asset for a company, nurturing data skills has to become the topmost priority for any aspiring to mid-level data scientist.
The feeling of self-satisfaction with your current skillsets can land you at a miserable spot where your company may choose a candidate who not only has better experience in using new analytics tools but also has a deeper understanding of the latest trends. This, in turn, can result as an end of your career.
To solve this problem, we asked multiple data scientists from the AIM Expert Network community, to share their key insight on how to avoid unusual pitfalls and get out of the cocoon to build a bulletproof career.
- Not defining the problem accurately
- Not nurturing the data
- Focusing only on coding
- Ignoring Visualization at the cost of Modeling
- Extreme focus on tools and technologies instead of fundamental concepts.
- Disregarding the latest trends
- Avoiding participation in external and community events
2020-03-06 04:30:00+00:00 Read the full story…
Weighted Interest Score: 3.5473, Raw Interest Score: 1.6942,
Positive Sentiment: 0.2515, Negative Sentiment 0.3177
Power Analytics Global & Molecula Announce Strategic Partnership
A recent press release reports, “Power Analytics Global, a next generation technology platform company that specializes in critical network infrastructure software monitoring, prediction, simulation and data-analytics, and Molecula, a Cloud Data Access company that simplifies, accelerates, and enables instantaneous, secure access to large, fragmented, and geographically distributed data sets to support the most demanding Machine Learning (ML), Artificial Intelligence (AI), and IoT workloads, announced today a strategic partnership.”
The release goes on, “The goal of the partnership is to deliver a combined offering for customers to cost effectively protect and enhance their critical revenue resulting in higher margins, customer retention and more informed capital allocation decisions. The combined offering will allow customers to drastically reduce the cost, time and complexity of accessing critical, operational data from globally distributed network assets and infrastructure. The hardened tools and real-time capabilities of this powerful combination enable our clients to incorporate, predict trends, and take action in real-time across relevant components of distributed data sets while lowering the hardware footprint needed to store, process and make decisions from the operational data streaming from these critical networks.”
2020-03-09 07:15:54+00:00 Read the full story…
Weighted Interest Score: 3.4889, Raw Interest Score: 2.6316,
Positive Sentiment: 0.1815, Negative Sentiment 0.3630
Top 10 Technical AI and Machine Learning Conferences in 2020
The AI & ML field is growing at a very fast rate, and as a research scientist or ML engineer, you definitely want to keep track of the latest research advances, especially in your area of interest. To stay aware of the most important research breakthroughs, you can follow our regular research summaries across the main ML research fields.
For connecting with other researchers and practitioners in your subject area, you will want to attend at least a few technical AI conferences during the year. For your convenience, we’ve compiled a list of the key AI & ML research conferences held in 2020…
2020-03-05 18:23:01+00:00 Read the full story…
Weighted Interest Score: 3.3991, Raw Interest Score: 1.8951,
Positive Sentiment: 0.2815, Negative Sentiment 0.0657
How does Data Governance Differ from Data Platform Governance?
Data Governance guides personnel in better managing data. The guidance is ensured through policy and ownership of data in an organization. The emphasis is on formalizing the Data Management function along with the associated data ownership roles and responsibilities.
Data Platform Governance defines and governs how data platforms, databases and data warehouses can be better planned, and managed. Information Technology takes the responsibility of deploying platforms. Procedures for platform governance are developed by IT and are aligned to the Data Management Strategy. These procedures can be as simple as a selection of Tool stack.
2020-03-09 07:35:36+00:00 Read the full story…
Weighted Interest Score: 3.3408, Raw Interest Score: 2.2272,
Positive Sentiment: 0.0891, Negative Sentiment 0.1336
Why the AI we rely on can’t get privacy right (yet)
Whiile artificial intelligence (AI) powered technologies are now commonly appearing in many digital services we interact with on a daily basis, an often neglected truth is that few companies are actually building the underlying AI technology. A good example of this is facial recognition technology, which is exceptionally complex to build and requires millions upon millions of facial images to train the machine learning models.
Consider all of the facial recognition based authentication and verification components of all the different services you use. Each service did not reinvent the wheel when making facial recognition available in their service; instead, they integrated with an AI technology provider. An obvious case of this is iOS services that have integrated FaceID, for example, to quickly log into your bank account. Less obvious cases are perhaps where you are asked to verify your identity by uploading images of your face and your identity document to a cloud service for verification, for example if you are looking to rent a car or open up a new online bank account.
2020-03-07 00:00:00 Read the full story…
Weighted Interest Score: 3.2170, Raw Interest Score: 1.1343,
Positive Sentiment: 0.1829, Negative Sentiment 0.2195
Data Science and ML Platform Market Heats Up
If you’re in the market for data science and machine learning tools, we have great news: The market is absolutely booming in 2020. With a ton of healthy competition, vendors are investing heavily to differentiate their products and drive innovation. The vibrant market is also diversifying, with separate tracks evolving for users with different skill levels and goals.
Gartner isn’t typically one to get overly exuberant about things. That’s just not the way in Stamford, Connecticut. But analysts with the storied firm opened up a bit in a recent report and stated that the market for data science and machine learning platforms is “beyond healthy” and “thrillingly innovative.”
“The broad mix of vendors offer a granular range of capabilities, with solutions appropriate for most levels of maturity,” the company wrote in its Magic Quadrant for Data Science and Machine Learning Platforms. “The definitions and parameters of data science and data scientists continue to evolve, and the space is dramatically different from this Magic Quadrant’s inception in 2014.”
2020-03-03 00:00:00 Read the full story…
Weighted Interest Score: 3.1809, Raw Interest Score: 1.9262,
Positive Sentiment: 0.4322, Negative Sentiment 0.0247
Real-Time Data Streaming, Kafka and Analytics Part 3: Effective Planning for Data Streaming Improves Data Analytics
Data stream processing is defined as a system performing transformations for creating analytics on data inside a stream. In Part 1 of this series, we defined data streaming to provide an understanding of its importance. In Part 2, we got a bit more technical in explaining data integration and ingestion into one of the more popular stream platforms, Apache Kafka. This final piece will explore the benefits of data stream processing with Kafka, as well as how to best plan for implementing data streams.
Companies who wish to take advantage of real-time data streams for analytics require a modern data architecture. This infrastructure can be managed through the emerging DataOps, which applies principles of lean manufacturing, DevOps and agile software development to data pipeline management. By leveraging DataOps, companies can implement a fully governed data management strategy that promotes team collaboration and use of data for business-driven data analytics. A direct benefit is that as individuals work together on data analysis, they gain greater understandings of the information and raise their own data literacy.
2020-03-04 00:00:00 Read the full story…
Weighted Interest Score: 3.1777, Raw Interest Score: 2.0331,
Positive Sentiment: 0.3916, Negative Sentiment 0.0602
Outsourced Trading: Buy-Side Questions Answered
The popularity of asset managers outsourcing their trading desks to third parties and execution providers is on the rise. Well documented now, consultancy Opimas estimates that a fifth of investment managers overseeing more than $50 billion will outsource at least parts of their trading by 2020. In addition, almost half of outsourced firms expect their outsource revenues to grow between 50-100% in the next few years, according to a recent study by Tabb Group. Yet some investment management firms are still hesitant about whether they should move some of their trading functions over to third parties. In order to help asset managers weigh up the pros and cons of outsourcing, we have taken a look at some of the key benefits in the context of maintaining a healthy marketplace and answered some of the most commonly asked questions posed by asset managers before making a decision.
2020-03-05 18:30:16+00:00 Read the full story…
Weighted Interest Score: 3.1694, Raw Interest Score: 1.4333,
Positive Sentiment: 0.4730, Negative Sentiment 0.2150
How to Deliver a Data Science Project Successfully
It is demanding to know where to begin once zoućve decided that, yes, you wish to dive into the fascinating world of data and AI. Just having a look at all the technologies you need to understand all the tools you’re supposed to master is enough to make you confused.
Well, luckily for you, creating your first data project is actually not difficult as it seems. Becoming data-powered is first and most foremost about having to learn the basic steps and following them to go from raw data to create a machine learning model, and in the end to operationalization.
Let’s jump into the following steps that will help you in successfully delivering a data science project.
2020-03-08 18:21:32+00:00 Read the full story…
Weighted Interest Score: 3.0904, Raw Interest Score: 1.5218,
Positive Sentiment: 0.1806, Negative Sentiment 0.1806
Vatican, DoD Weigh in on Ethical AI Principles in Same Week
The Vatican and the Department of Defense both took stances on AI ethics last week.
The Department of Defense on Monday held a press conference to announce its principles of AI ethics to guide development of new systems. The Vatican on Friday received support from IBM and Microsoft for its guidance for developers of AI rooted in Catholic social teaching.
The Rome Call for AI Ethics was drafted by the Pontifical Academy for Life, an advisory body to Pope Francis. It outlines six principles to define the ethical use of AI, to ensure that AI is developed and used to serve and protect people and the environment. Microsoft and IBM announced support for the charter, reported WSJPro on Feb. 28.
2020-03-05 22:30:48+00:00 Read the full story…
Weighted Interest Score: 3.0680, Raw Interest Score: 0.9656,
Positive Sentiment: 0.1408, Negative Sentiment 0.1408
The Overview of Artificial Intelligence in Medicine
Artificial intelligence in healthcare, just like AI in general, mimics neurons’ structure and human brain organization in a very simplistic but very powerful way. It approximates its conclusions without direct human input while analyzing complex medical data. The essence of AI usage in healthcare is to analyze relationships between prevention, treatment, and outcome of human illnesses. The AI in healthcare is specific since it cannot be disruptive in a way it can be in other industries. Doctors don’t want to be disrupted. They would rather adopt a tool that would ease their administrative burden so they could focus on their patients. Another set of tools, doctors would approve, are some assistance tools, that would help them with problematic differential diagnoses. Finally, tools that provide therapeutic and surgical assistance would also be in demand. As a conclusion, the process of implementing AI methodologies in the healthcare industry cannot be disruptive. It must be gradual, thoroughly tested, proven and understood. There are simply too many moral, ethical and legal implications for doctors to just embrace novelty in a way that is advertised and embraced in other industries.
2020-03-09 00:00:00 Read the full story…
Weighted Interest Score: 3.0385, Raw Interest Score: 1.1338,
Positive Sentiment: 0.3175, Negative Sentiment 0.1814
How AWS Nudged Out IBM, Google & Microsoft From The Cloud AI Space
The cloud space is ever-evolving, which in turn offers incredible opportunities for companies wishing to establish themselves as leaders in cloud computing. According to a report, the cloud market is expected to grow more than double in three years, to $195 billion by the end of 2020. A decade ago, the space was simply known as “cloud” comprising infrastructure-as-a-service for virtualised workloads, however, with the fractalisation of offerings, companies now need to be more specific in terms of what aspect of cloud they are dealing with.
In the recent era, artificial intelligence has been considered an important segment of the cloud space, which involves machine learning and deep learning. And, to analyse companies engaged in this space, Gartner, has come up with their report on the ‘AI Developer Services‘, which focuses on the platforms that deliver AI services via APIs. The companies involved in the study were Aible, AWS, Google, H2O.ai, IBM, Microsoft, Prevision.io, Salesforce, SAP and Tencent; however, Alibaba and Baidu were excluded for this analysis.
2020-03-09 11:30:00+00:00 Read the full story…
Weighted Interest Score: 2.8921, Raw Interest Score: 1.4754,
Positive Sentiment: 0.2350, Negative Sentiment 0.2350
“The 3 ingredients to our success.” | Winners dish on their solution to Google’s QUEST Q&A Labeling | Kaggle Winner’s Interview
Congratulations to the (four!) first-place winners of the Quest Q&A Labeling competition, Dmitriy Danevskiy, Yury Kashnitsky, Oleg Yaroshevskiy, and Dmitry Abulkhanov who make up the team “Bibimorph”!
In the QUEST Q&A Labeling competition by Google, participants were challenged to build predictive algorithms for different subjective aspects of question-answering. The provided dataset contained several thousand question-answer pairs, mostly from StackExchange. These pairs were human-labeled to reflect whether the question was well-written, whether the answer was relevant, helpful, satisfactory, contained clear instructions, etc. Results from the competition will hopefully foster the development of Q&A systems, contributing to them becoming more human-like. In this winner interview, we catch up with team Bibimorph to learn more about their approach to solving this unique challenge:
2020-03-05 14:47:40.061000+00:00 Read the full story…
Weighted Interest Score: 2.8394, Raw Interest Score: 1.4569,
Positive Sentiment: 0.2343, Negative Sentiment 0.2050
How AI will change the mobile app development industry
The tech world has been permeated by a plethora of disruptive technologies such as Artificial Intelligence, Machine Learning, AR/VR and so forth. The following post emphasizes on how the concept of AI seems to be revolutionizing the mobile app industry in one go!
We have reached 2020, a world that’s even more fast-paced and user-centric, a space that surely holds a wide range of promising trends for the industry ranging from chatbots to augmented reality. But above all, artificial intelligence steals the show due to its prompt, real-time access to the content aspect. Every industry using AI for some time now like eCommerce, retail, energy, mobile & software app development etc. Earlier smartphone applications incorporated cloud-based and internet-dependent solutions but now things have certainly changed and of course, for good.
Let me show you how!
2020-03-09 04:29:44+00:00 Read the full story…
Weighted Interest Score: 2.8025, Raw Interest Score: 1.3155,
Positive Sentiment: 0.4063, Negative Sentiment 0.0967
‘It Has Never Been Easier to Get into Machine Learning’ – Interview with Machine Learning Tokyo
As an Applied Linguist, Suzana Ilic was introduced to machine learning through her specialization in data and text analysis. Through working on projects in sentiment analysis and emotion recognition, she began writing code and now works on deep learning projects for natural language processing. Currently based in Tokyo, Japan, her work has seen her collaborate with companies like Google and research organizations such as RIKEN.
Ilic is also the founder of Machine Learning Tokyo, a non-profit organization that has brought together a passionate community of engineers and researchers to work on projects, study new technology, and gather for regular events with industry experts.
In this interview, we ask Ilic about ML trends in Japan, challenges facing engineers, how MLT grew into a community of +4800 members, and what’s in store for the future.
2020-03-07 22:00:58+00:00 Read the full story…
Weighted Interest Score: 2.7128, Raw Interest Score: 1.5421,
Positive Sentiment: 0.2341, Negative Sentiment 0.0964
Nobel laureate Robert Shiller identifies a rising ‘existential threat’ to the economy’s expansion — and tells us why it’s similar to what made the Great Depression so severe
The fear of artificial intelligence and its ability to displace workers pose an “existential threat” to our sense of economic strength, Robert Shiller, the Nobel Memorial Prize-winning Yale University economist, said.
He exclusively showed Business Insider the similarity between concerns about AI and the so-called technological-unemployment narrative that sprang up just before the Great Depression.
2020-03-06 00:00:00 Read the full story (Registration Wall)…
Weighted Interest Score: 2.6177, Raw Interest Score: 1.4554,
Positive Sentiment: 0.2117, Negative Sentiment 0.6880
A hybrid AI model lets it reason about the world’s physics like a child
A new data set reveals just how bad AI is at reasoning—and suggests that a new hybrid approach might be the best way forward. Questions, questions: Known as CLEVRER, the data set consists of 20,000 short synthetic video clips and more than 300,000 question and answer pairings that reason about the events in the videos. Each video shows a simple world of toy objects that collide with one another following simulated physics. In one, a red rubber ball hits a blue rubber cylinder, which continues on to hit a metal cylinder.
The questions fall into four categories: descriptive (e.g., “What shape is the object that collides with the cyan cylinder?”), explanatory (“What is responsible for the gray cylinder’s collision with the cube?”), predictive (“Which event will happen next?”), and counterfactual (“Without the gray object, which event will not happen?”). The questions mirror many of the concepts that children learn early on as they explore their surroundings. But the latter three categories, which specifically require causal reasoning to answer, often stump deep-learning systems. Fail: The data set, created by researchers at Harvard, DeepMind, and MIT-IBM Watson AI Lab is meant to help evaluate how well AI systems can reason. When the researchers tested several state-of-the-art computer vision and natural language models with the data set, they found that all of them did well on the descriptive questions but poorly on the others.
2020-03-09 00:00:00 Read the full story…
Weighted Interest Score: 2.5862, Raw Interest Score: 1.6863,
Positive Sentiment: 0.2353, Negative Sentiment 0.4706
DocuSign acquires Seal Software for $188 million
DocuSign announced its intent to acquire the contract analytics and AI technology provider Seal Software for $188 million in cash. The deal reflects the increasingly important role that artificial intelligence (AI) will play in digital document management.
The news builds on the existing relationship between the two companies. DocuSign already resells Seal’s flagship analytics and machine learning application as part of the DocuSign Agreement Cloud—its suite of applications and integrations for automating and connecting the entire agreement process. DocuSign also made a strategic investment in Seal in March last year.
With the acquisition, DocuSign can integrate Seal’s technology and value proposition more comprehensively across the Agreement Cloud—and therefore deliver greater value to companies looking to prepare, sign, act-on and manage the agreements that are critical to their business, the company said.
2020-03-04 09:38:50+11:00 Read the full story…
Weighted Interest Score: 2.5656, Raw Interest Score: 1.3046,
Positive Sentiment: 0.2552, Negative Sentiment 0.1134
Will Artificial Intelligence Render Human Transcriptionists Obsolete?
How will artificial intelligence, or AI, impact the transcriptionist industry? Here’s what to know about how AI compares to human transcriptionists. Artificial intelligence is changing the nature of human language. We are seeing computers that can understand language in very nuanced ways. Towards Data Science has a very interesting analysis of this trend in their article Understanding Natural Language Process, How AI Understands Our Languages. AI is still unable to grasp the complexities of language to the level of trained transcriptionists. However, that may change in the future. Will it eventually make them obsolete?
The world of professional transcription is something that is not to be taken lightly. Many companies in multiple industries put plenty of stock in a transcriptionist’s ability to do their jobs both quickly and accurately. It can be a demanding job that can be overwhelming for those who are unaware of just how challenging it can be.
2020-03-06 19:05:49+00:00 Read the full story…
Weighted Interest Score: 2.5353, Raw Interest Score: 0.9998,
Positive Sentiment: 0.1707, Negative Sentiment 0.3414
Rise of the Customer Data Platform
Retail businesses have moved from a product-centric model to a customer-centric model. This transformation has a significant impact on how companies are engaging with their customers who are spoilt for choice, well informed and tech-savvy.
Traditional approaches to managing customers were through Customer Relationship Management (CRM) processes and systems, however, in today’s hyperconnected world we see the emergence of Customer Data Platforms (CDP) as a robust model for handling customer data from a multitude of online and offline sources. The rising interest in Customer Data Platforms (CDPs) is reflected in the higher number of vendors providing these services as well as more venture capital funding (currently estimated at 2.4 billion USD).
What is a Customer Data Platform? Customer Data Platforms give a unified view of the customer from a multitude of touchpoints that are beyond the realm of traditional CRM systems. CDP’s can integrate data from both structured and unstructured sources, as well as online and offline sources to build a unified customer profile. The key here is traceability of the customer profile through the lifecycle of a customer and their interactions across the lifecycle. The key difference with CRM is its ability to handle wider touchpoints, ability to trace customers from site visitors to actual customers.
2020-03-09 06:31:00+00:00 Read the full story…
Weighted Interest Score: 2.5288, Raw Interest Score: 1.4288,
Positive Sentiment: 0.2023, Negative Sentiment 0.0253
Predicting 2020 Trends in Modern Data Architecture
From AI and machine learning, to data discovery and real-time analytics, a strong data architecture strategy is critical to supporting an organization’s data-driven goals.
Greater speed, flexibility, and scalability are common wish-list items, alongside smarter data governance and security capabilities.
DBTA recently held a roundtable webinar featuring Jeff Bayntun, manager, customer success, Simba Technologies, a Magnitude company; Suphatra Rufo, principal product marketing manager, Couchbase; and Ali LeClerc, director of product marketing, Alluxio, who discussed the top trends in modern data architecture for 2020.
Right now there is a bottleneck of market fragmentation, Bayntun explained, there are many different types of databases and database offerings to choose from.
2020-03-03 00:00:00 Read the full story…
Weighted Interest Score: 2.5034, Raw Interest Score: 1.5244,
Positive Sentiment: 0.1694, Negative Sentiment 0.1355
Women in finance: How AI is shining a light on diversity
While AI is shining a light on diversity, issues around bias and ensuing discrimination are in the spotlight too as the technology is being used in recruitment and is having a detrimental impact, questioning whether AI is ready to make hiring decisions on its own.
AI has helped in circumstances where there is a high-volume recruitment challenge and machine learning has supported the process of sifting through vast quantities of applications, with chatbots being implemented to answer candidate questions and screen applications at the first stages – claiming it is free of human prejudice and subconscious hiring bias.
However, technology can be as biased as humans if it replicates past hiring decisions and in the past, AI recruitment tools have realised that it discriminated against women because it attempted to find employees like its current workforce, namely, men.
While AI stands the chance of democratising access to capital for women, the irony is that only 22% of the AI workforce is made up of women. The financial services industry can address these imbalances by driving a greater focus on inclusion, empowerment and equality. Potentially, with more women working in the technology industry, writing algorithms and feeding into product development change, they can imagine and develop technology too.
2020-03-09 00:00:00 Read the full story…
Weighted Interest Score: 2.4596, Raw Interest Score: 0.9962,
Positive Sentiment: 0.4575, Negative Sentiment 0.3253
AI Weekly: Coronavirus, facial recognition, and the future of privacy
Global cases of COVID-19 surpassed 100,000 today. As President Trump signs into law an $8.3 billion emergency aid package to address the crisis, the chief of the World Health Organization (WHO) said yesterday that this is “a time for pulling out all the stops.” New cases are emerging in countries around the world, but COVID-19 appears to be flat or declining in China, where the novel virus first emerged. Earlier this week, VentureBeat took a look at some ways AI is being applied to fight COVID-19.
AI and big data played a significant role in China’s response to COVID-19, according to a WHO report compiled by about a dozen outside health professionals and released last month. The assessment finds that swift action by Chinese authorities to limit travel and quarantine entire cities potentially kept hundreds of thousands of people from being infected. But many also criticized China’s measures as draconian.
It’s unclear to what extent facial recognition played a role in enforcement of public safety in China, but a coauthor of the WHO study told Science that China is making strides on COVID-19 through “good old social distancing and quarantining, very effectively done because of that on-the-ground machinery at the neighborhood level facilitated by AI and big data.”
2020-03-06 00:00:00 Read the full story…
Weighted Interest Score: 2.4444, Raw Interest Score: 1.0398,
Positive Sentiment: 0.0912, Negative Sentiment 0.4196
Could Brexit open the gates to AI in the UK?
The UK is outwardly pursuing an adequacy decision from Europe regarding its current data protection and privacy regulations. However, the idea of eschewing outright assimilation with relevant EU laws is gaining traction as it provides an opportunity to sculpt a more appealing privacy framework. As it stands, come 31 December 2020, the UK will have departed the EU, and the transition period which ensured the stability of continuing UK/EU regulation to bridge the exit will cease.
In conversation with Finextra Research, Miriam Everett, partner and global head of data and privacy at Herbert Smith Freehills, refers to a statement made by the UK Prime Minister Boris Johnson in February 2020 in which he alludes to the possibility of altering the application of current privacy laws under GDPR: “The UK will in future develop separate and independent policies in areas such as[…]data protection, maintaining high standards as we do so…the UK would see the EU’s assessment processes on financial services equivalence and data adequacy as technical and confirmatory of the reality that the UK will be operating exactly the same regulatory frameworks as the EU at the point of exit.”
2020-03-09 11:01:00 Read the full story…
Weighted Interest Score: 2.4373, Raw Interest Score: 1.1207,
Positive Sentiment: 0.2537, Negative Sentiment 0.2396
Ex-AWS, Azure employees raise $3.3M for Seattle startup that helps companies save on cloud costs
Aran Khanna, Nikhil Khanna, and Daniel Christianto know a lot about the complexity of cloud computing, having worked for industry leaders such as Amazon Web Services and Microsoft Azure. Now the entrepreneurs are using their knowledge and expertise to help other companies save money on cloud-related costs. The three co-founders head up Reserved.AI, a Seattle startup that just raised $3.3 million to fuel growth. Amplify Partners and Pioneer Square Labs invested in the round.
Founded less than a year ago, Reserved.AI already has more than 20 customers who use its software to automate cost management of their AWS cloud spend. The startup says its clients average 35 percent savings. “The Reserved.ai product uses proprietary machine learning algorithms to analyze a customer’s AWS usage patterns and match them to an optimal set of AWS purchasing options, designed to maximize savings and minimize risk,” said CEO Aran Khanna, a Harvard grad who spent 18 months at Amazon as an AWS engineer.
2020-03-04 18:20:48+00:00 Read the full story…
Weighted Interest Score: 2.4025, Raw Interest Score: 1.3612,
Positive Sentiment: 0.0000, Negative Sentiment 0.1361
Top 10 Powerful Data Modeling Tools For 2020
Data science in 2020 allows business owners to process massive amounts of information and obtain the valuable nuggets that once took days to compute. With data modeling, you can take a complex software process and create a diagram that is much easier to understand.
If your business deals with big data at all, then data modeling is a concept you may already know. You can use tools for data modeling to create an overall IT strategy for your business or in the task of developing new databases.
2020-03-04 20:23:53+00:00 Read the full story…
Weighted Interest Score: 2.3719, Raw Interest Score: 1.5983,
Positive Sentiment: 0.2801, Negative Sentiment 0.0000
Big Data Is Changing The Way People Learn New Languages
Big data ischanging the way people learn, and the fact of the matter is that language is one of the most complicated yet sought after packets of information you seek. Because language and communication are so important, people will go and try different means to learn a new language. Using the ability of big data to access and process large sets of information, language can become much easier to learn and communicate.
2020-03-09 12:11:23+00:00 Read the full story…
Weighted Interest Score: 2.2599, Raw Interest Score: 1.4438,
Positive Sentiment: 0.2929, Negative Sentiment 0.3557
New Tech Platforms Hold the Key to Retail Banking’s Future
Today’s more powerful enabling technologies didn’t exist when most banks and credit unions implemented their current core platforms, EY notes in its report. the result has been mostly ad hoc deployment of AI and other cognitive technologies. Instead, they need to be managed as part of everyday IT operations, and modern core technologies enable this.
Battles tells The Financial Brand that the next big push in AI and machine learning will be extracting data from within banks’ own firewalls.
“If you have somebody’s primary checking account, you essentially understand their spending habits and can build a pro forma cash flow just with the data you have. That helps you direct [more personalized] offers to them.” He notes, however, that traditional institutions need to be sure their data models are supervised and certified — allowing them to “learn and adapt in the background at a pace that demonstrates the right amount of control.”
2020-03-02 00:13:10+00:00 Read the full story…
Weighted Interest Score: 2.2220, Raw Interest Score: 1.3680,
Positive Sentiment: 0.2098, Negative Sentiment 0.1277
K2 and Celonis Join Forces to Accelerate Digital Transformation
SourceCode Technology Holdings, Inc., the maker of K2 Software and a leader in intelligent process automation, and Celonis, a leader in AI-enhanced Process Mining and Process Excellence software, today announced a partnership leveraging process mining to accelerate digital transformation results for enterprises worldwide that are focused on mission-critical processes.
Integration of K2’s deep domain expertise and digital process automation capabilities with Celonis’ process mining capabilities will enable enterprises to better define and eliminate bottlenecks and inefficiencies. Customers can extract data from their operational systems and leverage powerful artificial intelligence (AI) and machine learning (ML) techniques to continuously optimize and adapt their business processes to accelerate digital transformation and deliver improved outcomes.
“As digital transformation initiatives drive the need for end-to-end automation, cataloging and prioritizing manual and inefficient automated processes becomes a logical starting point. Using process mining to underpin digital transformation automates daunting, error-prone manual discovery,” wrote Rob Koplowitz, vice president and principal analyst at Forrester Research in the February 2019 report, Process Mining: Your Compass For Digital Transformation.
2020-03-09 07:10:52+00:00 Read the full story…
Weighted Interest Score: 2.1529, Raw Interest Score: 1.9407,
Positive Sentiment: 0.3774, Negative Sentiment 0.2156
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