Mar 22, 2021
The benefits which MNCs are getting from AI/ML
Why to Explain AI/ML?
In the recent years, we see increasing demand in interpretable AI/ML. Human decision-makers would like to trust their AI-based decision support on the ground of rationale rather than via religious belief in what AI system calculates/suggests/forecasts.
It was quite easy in good old days when the stage was preoccupied by the easily interpretable ML algorithms like (Linear regression, polynomial regression, logistic regression, CART-based decision trees etc.).
However, such algorithms lacked accuracy in many real-world business forecasting and decision support scenarios. It resulted in the advent of highly accurate and complicated algorithms (starting from Random Forests through Gradient Boosting Machine-like algorithms up to the wide spectrum of Neural Networks of the second generation). However, the accuracy came at a price. There was no more easy way to interpret the decision-making flow of such AI/ML algorithms in a human-friendly and rational way.
One of the early attempts to address the challenge was adding the supplementary capability to calculate the feature importance scores by some of the modern algorithms (this is featured, for instance, by Random Forest, GBM, and lightgbm).
However, the feature importance scores are sometimes confusing / misleading due to the fact they are calculated separately from the ML model training itself.
Such a collision gave a birth to several analytical algorithms to calculate the feature importance / do the feature selection for ML models. As opposed to the classical statistical (filtering) approaches (where feature importance is determined on a basis of a certain statistical metric, whether it is a Pierson correlation or Chi Square), such techniques embrace a series of model training experiments under certain feature space tweaks. In such a way, they relatively score the importance of each feature for a specific model to train.
In this post, we are going to review such feature selection / feature importance detection methods. They all will be useful in the strive to build the industrial culture of interpretable AI/ML.
With this direction, we are on a par with the industry giant like Google (who recently launched the services of Explainable AI — see https://cloud.google.com/explainable-ai).
Artificial Intelligence and Machine Learning solutions offer many possibilities to optimize and automate processes, save money, and reduce human error for many industries. AI and ML can benefit restaurants, bars, and cafe businesses as well as in food manufacturing. These two segments have common use cases where AI in the food industry can be applied.
1. Chatbots:
Artificial intelligence continues to be a hot topic in the technology space as well as increasing its inception into other realms such as healthcare, business, and gaming. AI-powered chatbots in enterprises will also see an influx of people get more comfortable with how AI can actually benefit businesses versus, say, take away their jobs. From an analytical standpoint, AI can be incorporated into interfaces to change how they receive and understand data.
2. Artificial Intelligence in eCommerce:
Artificial Intelligence technology provides a competitive edge to e-commerce businesses and is becoming readily available to companies of any size or budget. Leveraging machine learning, AI software automatically tags, organizes and visually searches content by labeling features of the image or video.
3. AI to Improve Workplace Communication:
Current business communication is overloaded with content, channels, tools, and so-called solutions, depriving individuals (and companies) from hitting targets while also harming work-life balance. Artificial Intelligence will help businesses improve communication internally and externally by enabling individual personalization for each professional, allowing for enhanced focus and increased productivity.
4. Human Resource Management:
AI and Machine learning are going to drastically and irrevocably change how HR and recruitment work in every company and this is going to be awesome. In fact, HR is likely to be one of the first areas of business that will benefit from AI for two simple reasons. Firstly there are tons of top quality data in HR, and secondly, HR is one part of any company that is both essential and yet feels the pressure of time.
5. AI in Healthcare:
In the year ahead, and particularly in the next five to ten years, artificial intelligence is going to have a big impact on the healthcare industry and the ways in which healthcare related companies utilize AI. Here is a short note from Dr. Jeff Dunn, CEO of Redivus Health. Redivus Health is a transformative mobile app used by healthcare providers to prevent medical errors by offering both clinical decision support during critical medical events as well as documenting those events electronically in real time.
6. Intelligent Cybersecurity:
In regard to cybersecurity, Artificial Intelligence is making great strides. Although AI is considered to be in its infancy in cybersecurity and cannot always effectively address all issues, it works successfully in data protection. AI allows companies to detect vulnerabilities or anomalous user behavior in such business applications as ERP or Financial systems.
7. Artificial Intelligence in Logistics and Supply Chain:
When combined with customer data and analytics, physical artificial intelligence removes friction from the customer experience. Artificial intelligence empowers businesses to act on consumer data to drive improvements throughout many areas of supply chain operations. Mobile technology and the “Uberization” of things have made consumers hungry for AI.
8. Sports betting Industry:
In its article Sports trading and AI: Taking the human out of sports betting, Gambling Insider argues that, “Just as more scientific analysis of sport is changing how coaches, trainers, and clubs play their respective games, greater analysis of sporting events is helping odds making database operators evaluate the potential permutations of each sporting event, increasing the accuracy of that respective odd and thereby making the subsequent odds determination easier.”
9. Streamlined Manufacturing with AI:
For most customers when it comes to AI or Machine Learning, the magic happens when vast amounts of data can be streamed at milliseconds from the machine and process data of various databases. This provides actionable insights that can help these customers reduce non-productive downtime, predict failures or build a “golden batch” that can be benchmarked across all production lines.
10. Casino/Hotels/Integrated Resorts:
AI can help hotels/casinos discover customer segments that they may not realize were there. Which customers want to be near the pool, which ones need three morning papers before they can tackle the day. Armed with this kind of information, hotels can understand what matters the most to its guests at the individual level, enabling them to anticipate their guest’s needs before even the guests are aware of them.
11. Retail:
Shopping online creates rich data footprints regarding the individual preferences, spending habits and preferred channels of individual consumers. Feeding these digital breadcrumbs into an AI-engine helps bring curated shopping journeys to mass audiences. Automated bots can create lifelike, seamless customer service experiences, addressing the consumer on their purchase history and known preferences.
The MCS’s that use AI/ML are:
The online retail giant applies AI and ML technologies to improve both their products and services. Amazon Echo is one of their most popular AI-based products that use Alexa, an intelligent personal assistant. After acquiring Kiva, a robotics company in 2012, Amazon implemented an ML algorithm to automate their picking and packaging process. This brought down their ‘Click to ship’ cycle to just 15 minutes, thereby reducing operating costs by 20% while improving inventory capacity by 50%. The company also uses ML technology to identify workflows and enhance their customer interactions. Amazon also has a cloud computing division, Amazon Web Services, which offers AI services. With many AI and ML projects in their bucket, Amazon is one of the top AI companies to work for.
The IT company which featured among Fortune’s top 100 companies to work for in 2017 has big plans for AI. Nvidia’s products include computer chips and platforms with ARM/ GPU that can be used in a variety of devices from drones to automobiles. Their latest graphics processing unit (GPU), Titan V is one of the most powerful GPU of all time and can be used for research in AI and ML. The Glassdoor research ranks Nvidia at number 2 on their list of top companies hiring for AI talent.
As one of the leading software companies, Microsoft has been building its AI capabilities on different fronts to drive their business. With a variety of AI-based products and services like Cortona, CNTK, cognitive services, and industry-specific AI apps, Microsoft offers developers many interesting and challenging projects in AI.
4) IBM
Watson is IBM’s most well-known AI projects. IBM’s Watson division is focused on developing cloud-based artificial intelligence technologies for their own products and other organizations. The technology has been used in several spheres including cancer research and retail. IBM is investing heavily in developing their AI capabilities for a wide range of use cases from self-driving cars to hospitality.
Accenture is investing heavily in combining different technologies with AI and IoT. With the objective of developing AI-based solutions for its clients, Accenture has set up a global network of innovation hubs for developing AI technologies in San Jose, California, and Arlington, Virginia, in the United States; Sophia Antipolis, France; Beijing, China; Bangalore, India; and Dublin, Ireland.
With over 3 billion users, worldwide, Facebook is the leading social networking site in the world. The company recognized as one of the best places to work in 2018 by Glassdoor is also home to cutting-edge innovations in AI. Their internal group called Facebook AI Research (FAIR) is committed to solving challenges in AI. Apart from acquiring AI companies like Masquerade and Zurich Eye, the company has also invested strategically in their own artificial intelligence labs. The company’s AI research team led by deep learning pioneer, Yann LeCun has many major initiatives planned for 2018 to improve the efficiency of the social media platform.
Intel is investing big time in AI and ML technologies. Apart from developing new ML frameworks and AI chips, the company has invested in many AI startups and acquired AI-focused companies. Saffron Technology is one such company that was acquired by Intel. With a focus on building greater AI capabilities, Intel is among the top 10 companies hiring AI talent in the market.
The smartphone manufacturer is developing AI technologies to improve camera features, security and user experience of mobile phones. Their AI-powered assistant, Bixby, is designed to deliver a better user experience for mobile phone users. The company is also investing in AI-based startups and have set up AI research centers worldwide.
To leverage on AI and ML technologies for manufacturing, the company will invest $1.2 billion in the next two to four years. Their range of AI concept devices includes SmartCast+, an intelligent, interactive speaker that delivers AR experience. Apart from working with renowned tech universities, Lenovo has also set up specialized research labs in the US, Germany, and China.
Adobe has several new programs and projects focused on building better tools powered by AI. With their Sensei platform based on AI and ML, Adobe plans to offer better user experience to its clients. The company plans to incorporate more AI-based technology in its services and products.
By leading the AI revolution, these top AI company.
