iHack Alpha by Sentient.io unveils AI powered solutions for Business & Social Challenges

iHackAlpha Challenge

As a known fact, the retail scene globally has been in a slump ever since the rise of e-commerce and digital devices. The pandemic-stricken year has pushed the sector further into gloom, with experts predicting sales to be declining by at least 10% year-on-year in the Southeast Asia region. On top of that, businesses are grappling with the challenge…

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Everything you need to know about Google BERT

If you’ve been following developments in deep learning and natural language processing (NLP) over the past few years then you’ve probably heard of something called BERT; and if you haven’t, just know that techniques owing something to BERT will likely play an increasing part in all our digital lives. BERT is a state-of-the-art embedding model published by Google, and it represents a breakthrough in the field of NLP by providing excellent results on many NLP tasks, including question answering, text generation, sentence classification, and more. 

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The importance of being prepared with Artificial Intelligence at Biomedical Engineering

Book/eBook: Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models by Jorge Garza-Ulloa- Academic Press(R) -Elsevier 2021Classically, a “physician” is defined as a professional who possesses special knowledge and skills derived from rigorous education, training, and experience, in other words “medical education remains based on information acquisition and application”.

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C3.ai exec says lack of automation is holding back AI progress

Model driven architecture

After more than a decade of providing a platform-as-a-service (PaaS) environment for building and deploying AI applications, C3.ai launched an initial public offering (IPO) in December 2020. Earlier this month, in partnership with Microsoft, Shell, and the Baker Hughes unit of General Electric, the company launched the Open AI Energy Initiative to enable organizations in the energy sector to more easily share and reuse AI models.

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Nuance acquires Medical Voice Assistant Startup Saykara

Saykara Nuance

Nuance announced that it had acquired healthcare artificial intelligence startup Saykara for an undisclosed sum. The acquisition will bring the tech underlying Saykara’s voice assistant for doctors to Nuance’s conversational AI fro medical professionals, specifically the ambient clinical intelligence (ACI) designed to reduce doctor burnout.

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My thoughts on the AI Revolution: Hype, scams, and big brother

Character in Mask

What is behind Artificial Intelligence?
Artificial intelligence can be defined as a science that models intelligent human behavior. This definition may have one significant drawback — the concept of intelligence is difficult to explain in principle. The problem of defining artificial intelligence comes down to the problem of defining intelligence in general: is it something in common, or does this term combine a set of disparate abilities, and even more as individual or even collective abilities?

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IBM, Palantir partner on AI Apps

AI Apps

IBM and data analytics software vendor Palantir Technologies Inc. will release a cloud data platform in March designed to deploy AI-based applications built around Watson. The service, dubbed Palantir for IBM Cloud Pak for Data, will run on Red Hat OpenShift, enabling hybrid cloud deployments, the partners said Monday (Feb. 8). It also integrates Palantir’s Foundry operations platform designed to integrate data management with analytics.

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Kong raises $100 million for software that scales cloud infrastructure

As APIs and microservices become critical tools to drive innovation and automation for a wider range of companies, they are also creating new management challenges. Enterprises are attracted by their potential to create greater flexibility but must find ways to coordinate these cloud-based services. Kong, one of the new breed of companies trying to address this problem…

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Artificial Intelligence — Agents and Environments

Agents - Environments

Agent and Environment are two pillars in Artificial Intelligence, our aim is to build intellectual agents and work in an environment. If you consider broadly agent is the solution and environment is the problem. In simple terms, even starter or researcher can understand that and is defined Agent as game and Environment as ground.

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Microsoft details Speller100, an AI system that checks spelling in over 100 languages


In a post on its AI research blog, Microsoft today detailed a new language system, Speller100, that the company claims is one of the most comprehensive ever made in terms of language coverage and accuracy. Comprising a number of AI models that can understand speech in over 100 languages collectively, Speller100 now powers all spelling correction on Bing.

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3 ways Leaders fail their AI Projects

Girl with two eggs

Why do so many AI Projects fail, and how can Leaders avoid this? How do most organizations begin their Artificial Intelligence (AI) journey? Let’s look at how leaders of some large enterprises planned their foray into AI. Here are a couple of recent examples from McKinsey:

How do most organizations begin their Artificial Intelligence (AI) journey?
Let’s look at how leaders of some large enterprises planned their foray into AI. Here are a couple of recent examples from McKinsey:
The leader of a large organization spent two years and hundreds of millions of dollars on a company-wide data-cleansing initiative. The intent was to have one data meta-model before starting any AI initiative.
The CEO of a large financial services firm hired 1,000 data scientists, each at an average cost of $250K, to unleash AI’s power.
And here’s an example that I witnessed first-hand.
The CEO of a large manufacturer lined up a series of ambitious projects that used unstructured data, since AI techniques are very effective with text, image, and video data.
What do all of these initiatives have in common? They all failed.

McKinsey’s State of AI survey found that only 22 percent of companies using AI reported a sizable bottom-line impact.

In addition to the massive sunk costs suffered by these projects, they led to the organization’s disillusionment with advanced analytics.
This is not uncommon. McKinsey’s State of AI survey found that only 22 percent of companies using AI reported a sizable bottom-line impact. Why do so many projects fail, and how can leaders avoid this?
Most leaders pursuing AI miss out on three areas of ownership. These responsibilities start well before you plan your AI projects, and they extend long after your projects go live.
Here are the three ways to fail your AI initiative:

Photo by 青 晨 on Unsplash
McKinsey found that only 30 percent of organizations aligned their AI strategy with the corporate strategy. Isn’t it shocking that a majority of leaders are burning their cash in the name of AI? Organizations often pursue AI initiatives that appear interesting or those that are just urgent.
True, your projects must address a business pain point. But, what’s more important is that these outcomes must align with your corporate strategy. Start with your business vision and identify how data will enable it. Clarify who your target stakeholders are and define what success will look like for them.

Organizations often pursue AI initiatives that appear interesting or those that are just urgent.

Then, identify the strategic initiatives that will empower the stakeholders and get them closer to their business goals. Now, you’re ready to brainstorm to come up with the long list of AI projects that are worth evaluating.
In a report by MIT Sloan Management Review, Steve Guise, the CIO of Roche Pharmaceuticals, explains how AI helps transform the company’s business model. Roche is working toward making personalized health care a reality. Guise points out that the current model of drug delivery will not help them achieve this vision. They see a need to accelerate the pace of drug discovery from three drugs per year to 30. Guise says that AI can help them get this exponential improvement.
Roche is making AI mainstream within the organization by building capabilities across screening, diagnosis, and treatment. It augments this by partnering with startups pursuing AI-driven drug discovery,. Thanks to these efforts, Roche has made significant breakthroughs in the treatment of diseases such as Hepatitis B and Parkinsons. By starting with their corporate vision and aligning all their AI initiatives with this overarching objective, Roche’s efforts are bearing fruits.

Photo by KS KYUNG on Unsplash
When should you think about Return on Investment (ROI) from your AI project? Most organizations make the mistake of tracking ROI when the project goes live. Leaders settle for fuzzy outcomes such as “efficiency improvement,” “brand value,” or “happier customers,” to make matters worse.
True, it’s not easy to quantify the dollar value of outcomes. But it’s not impossible. You must demand quantification of business benefits even before greenlighting a project. AI can deliver value by either growing revenue or lowering expenses. Both are valuable. Define which of these outcomes your project will enable.

Leaders make the mistake of settling for fuzzy outcomes.

Identify a mix of leading and lagging metrics that will help measure these outcomes. Collect the data needed to compute the metrics by updating your processes or creating new ones. Finally, track your investments by going beyond the hardware, software, and technical team costs. Include your spending on adoption and change management programs. This ROI metric should be a critical factor in your project approval decision.
Deutsche Bank rolled out its AI-driven consumer credit product in Germany. The solution made a real-time decision on the loan even as the customer filled out the loan application. Consumers were worried about loan denials impacting their credit ratings. This product removed that risk by telling them whether their loan would be approved, even before they hit “apply.”
Deutsche Bank found that loan issuance shot up by 10 to 15-fold in eight months after the AI-powered service was launched. The gains were achieved by bringing in customers who wouldn’t have applied in the first place. This was a clear case of AI helping grow revenue.

Photo by Tengyart on Unsplash
In its 2019 annual survey, Gartner asked Chief Data Officers about their biggest inhibitor to gaining value from analytics. The topmost challenge had nothing to do with data or technology. It was culture.
As Peter Drucker famously said, “Organizational culture eats strategy for breakfast.” Even the best-laid AI strategy will amount to nothing if you don’t carefully shape the organizational culture. A culture change must start at the top. Leaders must use storytelling to inspire and demonstrate how AI can help the organization achieve its vision.

Leaders must address the fear around AI and improve the data literacy of all employees.

Leaders must address the fear around AI and improve the data literacy of all employees. They must lead by example and sustain change by onboarding data champions across all levels. The cultural shift takes years, and leaders must influence it long after the projects have gone live.
Wonder what the main ingredient in a Domino’s Pizza is? It’s data! Dominos Pizza is the poster child of technology transformation. The organization lives the data-driven decision-making culture and uses AI across sales, customer experience, and delivery. This wasn’t the case 10 years ago.
Patrick Doyle took over as CEO of the 50-year old pizza maker in 2010 when it was panned by customers and investors alike. Doyle took the bold step of going public with the harvest reviews. He then did a full reboot inside-out and set the organization on the path of digital transformation. He placed some bold bets on technology by taking on risky projects, empowering people, and building several AI innovations in-house.
When Doyle retired in 2018, Dominos’ sales had increased for 28 quarters straight, and it delivered stock returns that outpaced Google’s. The outgoing CEO summed it up best, “We are a technology company that happens to sell pizza.” By leading a cultural transformation within Dominos, Doyle ensured a shift to data-driven decisions that has sustained even after he transitioned to a new CEO.

Photo by Craig Chelius from Wikimedia Commons
Adoption of technology innovation is never easy. Whether it’s the launch of new technology such as AI in the marketplace or its adoption within an organization, the challenges are similar.
Innovators seed this journey within an organization. The innovation is then embraced by early adopters, thanks to their initial enthusiasm and openness to change. But then, the pace slows down and enters a chasm. There often is a lack of visibility, uncertainty in outcomes, and broader resistance to change.
This is where most initiatives fail.
For an innovation like AI to cross this chasm and go mainstream, it needs leadership intervention. Leaders must make AI successful by aligning the initiative with their corporate vision. They must demonstrate economic value by institutionalizing conversations on ROI from AI. Finally, they must shape the organizational culture to facilitate change and enable the viral adoption of AI-driven decision making.

Photo by Paige Cody on Unsplash

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Uber Fiber is an Open Source framework to distribute compute for Reinforcement Learning Models

Uber Fiber

Computational costs are one of the main challenges in the adoption of machine learning models. Some of the recent breakthrough models in areas such as deep reinforcement learning(DRL)… constrained to experiments in big AI research labs. For DRL to achieve mainstream adoption, it has to be accompanied by efficient distributed computation methods that effectively address complex computation requirements. Recently, Uber open sourced Fiber, a scalable distributed computing framework for DRL and population-based methods.

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Monte Carlo raises $25 million for AI that monitors data reliability

San Francisco-based data reliability startup Monte Carlo today announced that it raised $25 million, bringing the company’s total capital raised to date to over $40 million. Monte Carlo says the proceeds will allow it to foster its community of users and further develop its data and analytics products as it looks to expand the size of its workforce.
It’s estimated that the average company spends upwards of $15 million annually tackling periods of time where data is missing, broken, or otherwise inaccurate.

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Digital Owl emerges from stealth with AI that analyzes and summarizes medical records

Medical pack on table

Digital Owl, a provider of AI-powered medical claim analysis software, today emerged from stealth with $6.5 million. The company plans to use the funding, a seed round, to expand its workforce and further develop its technology platform.
In health care, the processes of underwriting and claims analysis are labor-intensive and error-prone. Claim adjusters and underwriters are required to read and carefully parse hundreds of documents per case. Each year, the insurance market invests an estimated more than $3 billion in work hours devoted to collating and summarizing medical records.

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2021: Emerging AI trends in the telecom industry

AI 2021

No longer limited to providing basic phone and Internet service, the telecom industry is at the epicenter of technological growth, led by its mobile and broadband services in the Internet of Things (IoT) era. This growth is expected to continue: the IoT telecom services market was estimated to grow from $2.90 billion in 2016 to $17.67 billion in 2021, at a CAGR of 43.6%. The driver for this growth? Artificial intelligence (AI).

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