1608215899
Guide To Qlib: Microsoft’s AI Investment Platform
#tutorial
Here is your hand-on-guide to Qlib - Microsoft’s AI Investment Platform. It will allow users to easily try their ideas to create better Quant(Quantitative trading analysts) investment strategies.
#ai #microsoft #fintech #investment #tech #qlib
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1608215899
Guide To Qlib: Microsoft’s AI Investment Platform
#tutorial
Here is your hand-on-guide to Qlib - Microsoft’s AI Investment Platform. It will allow users to easily try their ideas to create better Quant(Quantitative trading analysts) investment strategies.
#ai #microsoft #fintech #investment #tech #qlib
1597074133
Microsoft Reveals Need To Prioritise Skills To Maximise Value From AI
Microsoft India today released new research revealing that organisations that combine the deployment of AI with skilling initiatives are generating most value from AI. The topline findings of the research underscore that mature AI firms are more confident about the return on AI and skills.
The tech giant recently conducted a global survey with approximately 12,000 people working with enterprise companies. The research surveyed employees and leaders within large enterprises across industry verticals in India, and 19 other countries, to look at the skills needed to thrive as AI becomes increasingly adopted by businesses, as well as the key learnings from early AI adopters.
The survey found a direct link between having the skills needed to thrive in an AI world and the value organisations gain from their AI implementations. The research further reveals that employees are keen to acquire AI relevant skills that are growing in importance and are of value to them personally and to the business. The organisation leaders surveyed predicted that half of all employees will be equipped with AI skills in the next 6-10 years, which is nearly one-and-a-half times more than the present estimations.
#news #ai research for businesses #ai survey #microsoft #microsoft ai for business survey #microsoft ai research #microsoft survey
1603785600
Microsoft’s Turing Language Model Can Now Interpret 94 Languages
Recently, the developers at Microsoft detailed the Turing multilingual language model (T-ULRv2) and announced that the AI model has achieved the top rank at the Google XTREME public leaderboard.
The Cross-lingual TRansfer Evaluation of Multilingual Encoders, also known as XTREME benchmark includes 40 typologically diverse languages, which span 12 language families. XTREME also consists of nine tasks that require reasoning about different levels of syntax as well as semantics.
The Turing multilingual language model (T-ULRv2) is created by the Microsoft Turing team in collaboration with Microsoft Research. The model is also known to beat the previous best from Alibaba (VECO) by 3.5 points in average score.
Saurabh Tiwary, Vice President & Distinguished Engineer at Microsoft mentioned that in order to achieve this milestone, the team leveraged StableTune, which is a multilingual fine-tuning technique based on stability training along with the pre-trained model. The other popular language models on the XTREME leaderboard include XLM-R, mBERT, XLM, among others. Ming Zhou, Assistant Managing Director at Microsoft Research Asia, stated in a blog post that the Microsoft Turing team has long believed that language representation should be universal. Also, this kind of approach would allow for the trained model to be fine-tuned in one language and applied to a different one in a zero-shot fashion.
For a few years now, unsupervised pre-trained language modelling has become the backbone of all-natural language processing (NLP) models, with transformer-based models at the heart of all such innovation. According to Zhou, this type of models has the capability to overcome the challenge of requiring labelled data to train the model in every language.
How T-ULRv2 Works
The Turing multilingual language model (T-ULRv2) model is the latest cross-lingual innovation at the tech giant. It incorporates the InfoXLM (Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training),which is a cross-lingual pre-trained model for language understanding and generation to create a universal model that represents 94 languages in the same vector space.
TT-ULRv2 is a transformer architecture with 24 layers and 1,024 hidden states. The architecture also includes a total of 550 million parameters. The pre-training of this model includes three different tasks, which are multilingual masked language modelling (MMLM), translation language modelling (TLM) and cross-lingual contrast (XLCo).
#developers corner #google xtreme #microsoft #microsoft ai #microsoft ai model #microsoft turing nlg #t-ulrv2 model #turing multilingual language model
1625958420
Selecting a Conversational AI Platform
Businesses are quickly acknowledging the importance of Conversational AI (CAI) to increase their customer engagement and revenues. The question is no longer whether to deploy CAI, but rather which platform to use and how to leverage its capabilities.
In this series, Daniel Eriksson, Chief Innovation and Customer Success Officer at Artificial Solutions, gives insight on important aspects of a conversational AI platform that buyers often overlook. For example: what does language support really mean? What is localization? How do different deployment models impact the TCO? And maybe most importantly, how can the CAI platform not only help me during the first development sprints, but across the entire bot lifecycle?
Making Bot Developers More Productive
During the last six months, I’ve had a lot of conversations with companies (clients) and system integrators (partners) who have been building conversational bots. I’ve spoken with conversational bot developers, data linguistics reps, integration engineers, conversational designers, project managers, senior stakeholders, product owners, and many more.
At the same time, I’ve talked to existing, new, prospective, and former clients. These talks included people who had ambitious plans and succeeded and others who have had plans where they have struggled to generate impact.
Four Perspectives to Consider When Selecting your Conversational AI Platform
Select a Tool Your Development Team Can Grow With
See past the buzz-words like “awareness”, “understanding”, and “self-learning”.
Conversational AI is a fascinating space and still holds a lot of potential that is yet to be explored. Yet most companies who have experience of CAI tooling will tell you it’s all about engineering, and actually has a lot of resemblance to regular software or process flow development instead of being something ground-breaking new.
Sure, there are some terminologies both useful and specific for the space, like “intent recognition”, “entities”, and “context”. These words are related to the Natural Language Understanding (NLU) part of a conversational bot.
Find a Balance Between Pure Coding and Drag-and-Drop
Have you ever heard about low-code or no-code? In short, those concepts describe a user interface where a developer can configure or graphically design a process instead of having to write programming code. It is a great way to visualize how a program is executed and can be a quick way to build some things rapidly. Here comes the tricky part — for an effective Conversational AI solution with some ambition, you will still need to code. Your team will need to write code in some scripting language. If not, you will not be able to do the things you expect a bot to do. Do not shy away from this fact, as scripting and coding are super important to make a bot great. So, when you look at a toolset, evaluate it from the standpoint “how will the coding part work?”
Consider Possible Future Limitations
There is a lot of CAI tooling in the market today available to developers. Your job is to make sure that you don’t select tooling that is quick to build only the first MVP but also is useful for every new generation of your bot. When your ambitions grow, and your insights on how you can deliver a better bot user experience start to develop, you might realize that the tool you chose is holding you back.
#ai #artificial intelligence #natural language processing #conversational ai #ai platform #platform
1619511840
Making Sales More Efficient: Lead Qualification Using AI
If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.
AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.
#ai-solutions-development #artificial-intelligence #future-of-artificial-intellige #ai #ai-applications #ai-trends #future-of-ai #ai-revolution