The excitement of using Artificial Intelligence has not dwindled from the time it has been unfolded. In KPMG study on “living in the AI world 2020: achievements and challenges of AI across 5 industries (retail, financial service, healthcare, transportation, and technology), revealed that 92% of respondents agreed that leveraging the spectrum of AI technologies will make their companies run more efficiently. Amidst the admiration towards AI, IBM created the Women Leaders in AI program in 2019. This was a way to acknowledge the women leading in AI and encourage females to lend a hand in the field of AI. Through this IBM, planned to make the efforts of the honourees more visible to the world. 2020 IBM women leaders were honoured for outstanding leadership in the AI space. Here is the list of women leaders in AI 2020 honorees:- Aarthi Fernandez Who is a Global head of Trade Operations and SEA Trade COO at Standard Chartered Bank? She is a C-suite executive with deep insight on how digitalization can positively disrupt US$17 trillion global trade. She is into deploying AI/Machine learning to make trade financing simple, faster, and better for corporate clients and mitigate compliance risk. Piera Valeria Cordaro She is a commercial Operations Innovation Manager, Wing Tre S.p.A., Italy. She is a speaker, advocating the use of AI in customer operations. Along with her team and with support by IBM Watson, implemented two chatbots, to improve customer experience. Both bots have made it possible to handle a million queries efficiently. Amala Duggirala Who is the enterprise Chief operation and Technology officer, Regions Bank, United States. To handle customers’ inquiries she deployed IBM Watson’s assistant- virtual banker persona, ”Reggie”. From the time of its implementation 4.3 million customer calls have been answered, with 22% of them being handled by AI. Mara Reiff Vice President, Strategy and Business Intelligence, Beli Canada, Canada. She used AI to improve operations, loyalty, and brand. She worked with IBM to install Watson studio Local using Red Hat open shift. This resulted in smarter, fast decision-making with improved customer experience leading to increased sales. Mara suggests everybody to “Make sure to stop and smell the roses. Take each opportunity to learn something new and embrace change”. Amy Shreve- McDonald She is lead Product Marketing Manager for Business Digital experience, AI&T, USA. EVA (Enterprise Virtual Agent) was launched in February 2019, to improve customer chat experience, it uses Watson assistant. This system has been able to handle 45% chats on its own, resulting in reduced costs and expanding 24/7 support. She also received AT&T’s 2019 Visionary Award for her work advocating EVA. Ryoko Miyashita Manager, customer service department, customer service section JACCS CO., LTD Japan. She launched a Watson-enabled operator onboarding tool, that resulted in reduced new operator training period by 30%. The tool has increase customer satisfaction. Her advice to the younger self is “It is important to believe in yourself, but it is equally or more important to believe in people around you. I would encourage myself to have many experiences and garner knowledge to objectively evaluate things, not blindly accept or exclude others’ opinions”. Carol Chen She is Vice President for Global Marketing, Global Commercial, Royal Dutch Shell, United Kingdom. Along with her team, Carol is partnering is planning for digital transformation with the creation of “Oren”- a Smart Minning Platform, by partnering with IBM. This platform will offer an innovative and creative experience for users in the sector to deliver connectivity and integration across the ecosystem. To use AI, she advice commencing with analyzing the business outcome that one wants and customer pain points that one can cater to. The next step would be to determine how to leverage AI and data to solve the problem. Rosa Martinez Cognitive Project Manager, CiaxaBank, Spain. For those who consider using AI, her advice to them is ‘first to understand the business case as it may take time more than expected. This phase can result in a non-AI project example a ‘software as usual’. But moving further with the project there can be more AI application for sure to work on’. Lee- Lim Sok Know Deputy Principal, Temasek Polytechnic, Singapore. Under the leadership of Sok Keow, The higher education institution in Singapore ‘Temasek Polytechnic’ launched the “Ask TP” chatbot in January 2018. The chatbot helped current as well as prospective students to get answers to the questions asked about Temasek and also gave personalized course advice. In the 1st two weeks of 2020, ‘Ask’ TP’ responded to more than4,351 questions. She suggests everybody “deeply appreciate ‘people’ as they are the most critical asset in an organization, and a leader must develop a team”. Itumeleng Monale Executive Head of Enterprise Information Management Personal and Business Banking, Standard Bank of South Africa, South Africa. By deploying many analytical tools in her organization, she can uplift the revenue of the company. Through models of analytics relationships, bankers are experiencing a 40% revenue uplift when comparing to their peers. She sees AI as a tool through which business delivery can be accelerated, value could be added to human capital and relationships can build further. With this AI era, Research has postulated that corporate giants still have less percentage of women in the technical department. Facebook’s diversity report suggests that there are 22 % of women in the technical department and 15 per cent of women work in the AI research group. Similarly, Google’s diversity report suggests that only 10% women are working on “machine intelligence”. There is a need to encourage women participation as there are many more women around the world, stepping out of the pre-existed sheathe and going beyond the walls to shape the future. Opening up the AI platform for all will fetch us more talented beings which can help us celebrate the use of AI in different fields and different ways. Reference:- https://www.ibm.com/watson/women-leaders-in-ai/2020-list https://advisory.kpmg.us/content/dam/advisory/en/pdfs/2020/technology-living-in-an-ai-world.pdf About the author:- Kirti Kumar is a budding HR professional currently pursuing PGDM in HR and Marketing at New Delhi Institue of Management. She looks forward to opportunities that can hone her skills. She is agile in her attitude with versatility in her action
This article is part of "AI education", a series of posts that review and explore educational content on data science and machine learning. How much math knowledge do you need for machine learning and deep learning? Some people say not much. Both are correct, depending on what you want to achieve. There are plenty of programming libraries, code snippets, and pretrained models that can get help you integrate machine learning into your applications without having a deep knowledge of the underlying math functions.
Orange is an open-source, GUI based platform that is popularly used for rule mining and easy data analysis. The reason behind the popularity of this platform is it is completely code-free. Researchers, students, non-developers and business analysts use platforms like Orange to get a good understanding of the data at hand and also quickly build machine learning models to understand the relationship between the data points better. Orange is a platform built on Python that lets you do everything required to build machine learning models without code. Orange includes a wide range of data visualisation, exploration, preprocessing and modelling techniques. Not only does it become handy in machine learning, but it is also very useful for associative rule mining of numbers, text and even network analysis.
No-code environments in machine learning have become increasingly popular due to the fact that almost anybody who needs machine learning, whatever field they may be in, can use these tools to build models for themselves. WEKA is one of the early no-code tools that was developed but is very efficient and powerful. WEKA can be used to implement state of the art machine learning and deep learning models and can support numerous file formats. In this article, we will learn about how to use WEKA to pre-process and build a machine learning model with code. WEKA can be used in Linux, Windows or Mac operating systems and you can download this from the official website here.
The development of the internet over the last few decades has resulted in a massive increase in the production of data and the unprecedented availability of computing power for corporate applications. Machine Learning and artificial intelligence (AI) techniques have been fuelled by these revolutions to emerge from being purely academic topics of investigation to be the basis for a new wave of products and services for the digital age. The paradigm-shifting opportunities presented to corporates by this emerging technology range from the ability to expose and extract insights and patterns from data lakes to replacing human beings in critical decision-making scenarios. However, with these opportunities also come novel risks and concerns that must be considered when contemplating the development and deployment of AI and machine learning agents. These include understanding how their trustworthiness may be measured, the ethics and policies required for their deployment and the cybersecurity implications of their widespread adoption.
In the 2002 sci-fi movie Minority Report, Tom Cruise's policeman character is able to see crimes before they are committed and arrest murderers before anyone gets killed. For Winvic, the future is using artificial intelligence (AI) to spot construction site accidents before anyone gets hurt. Winvic is working on the government-funded project with the University of the West of England (UWE Bristol) Big Data Enterprise & Artificial Intelligence Lab (Big-DEAL) and Bristol industrial intelligent video specialists One Big Circle. Their project uses real-time images and machine learning technologies to detect, recognise and track hazards on a construction site, which will then alert nearby operatives via Internet of Things enabled, global positioning system (GPS) devices. Dubbed as Computer-Vision-SMART, the'Computer Vision and IoT for Personalised Site Monitoring Analytics in Real Time' project will run for two years thanks to a £600,000 grant from Innovate UK.
Companies in all business sectors are competing to recruit top-notch AI teams, but are these investments productive? With millions worldwide working in AI now, and over 90% of mid-size and larger companies having specialized AI or Data Science teams, researchers and engineers in this field are literally drowning in the pace of innovation. Per day, an AI expert needs to scan several hundred new research publications to stay up to date. Leading researchers, like Yoshua Bengio and Yann LeCun, openly admit they find it impossible to keep up. Amsterdam based startup Zeta Alpha is now launching AI Research Navigator, a new deep learning-based search platform, to help AI experts with this.
I really don't want to say that I've figured out the majority of what's wrong with modern education and how to fix it, BUT When we train (fit) any given ML model for a specific problem, on which we have a training dataset, there are several ways we go about it, but all of them involve using that dataset. Say we're training a model that takes a 2d image of some glassware and turn it into a 3d rendering. We have images of 2000 glasses from different angles and in different lighting conditions and an associated 3d model. How do we go about training the model? Well, arguable, we could start small then feed the whole dataset, we could use different sizes for test/train/validation, we could use cv to determine the overall accuracy of our method or decide it would take to long... etc But I'm fairly sure that nobody will ever say: I know, let's take a dataset of 2d images of cars and their 3d rendering and train the model on that first.
In machine learning when we build a model for classification tasks we do not build only a single model. We never rely on a single model since we have many different algorithms in machine learning that work differently on different datasets. We always have to build a model that best suits the respective data set so we try building different models and at last we choose the best performing model. For doing this comparison we cannot always rely on a metric like an accuracy score, the reason being for any imbalance data set the model will always predict the majority class. But it becomes important to check whether the positive class is predicted as the positive and negative class as negative by the model.
End-to-end Deep Reinforcement Learning (DRL) is a trending training approach in the field of computer vision, where it has proven successful at solving a wide range of complex tasks that were previously regarded as out of reach. End-to-end DRL is now being applied in domains ranging from real-world and simulated robotics to sophisticated video games. However, as appealing as end-to-end DRL methods are, most rely heavily on reward functions in order to learn visual features. This means feature-learning suffers when rewards are sparse, which is the case in most real-world scenarios. ATC trains a convolutional encoder to associate pairs of observations separated by a short time difference. Random shift, a stochastic data augmentation to the observations is applied within each training batch.