Data, and more importantly, the way people use it, is shaping and refining approaches to COVID-19 safety. Here’s a closer look at how this is happening.
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What are the main differences between artificial intelligence and machine learning?
Both the terms artificial intelligence and machine learning belong to the most-used buzzwords these days.
Undeniably, both the terms artificial intelligence and machine learning belong to the most-used buzzwords these days. Almost every tech organization is using these terms when talking about their products or services. Unfortunately, there’re still lots of confusion within the common people about what are these two exactly. Let’s go through the key differences between artificial intelligence and machine learning.
- Artificial intelligence is the intelligence demonstrated by machines. Any machine that understands its environment and is able to take actions that increase its chances of achieving some goals, can be described as an artificial intelligence-enabled machine. On the other hand, machine learning is one of the present applications of artificial intelligence.
- When machine learning goes beyond simple programming and can mirror and interact with people, even on the fundamental level, artificial intelligence comes into the picture. Though artificial intelligence needs machine learning to optimize decision, the former is the step beyond the latter. Artificial intelligence utilizes what it has obtained from machine learning to simulate intelligence.
- In artificial intelligence, a machine learns by gathering knowledge and understanding how to apply it. Here, the goal is to increase the chances of finding an optimal solution. It’s the study of training computers to try to do things which a human can do better at present. On the contrary, in machine learning, algorithms obtain the skill or knowledge via experience. It depends on big datasets to keep on reminding the data to identify common patterns.
- Based on capabilities, artificial intelligence can be distributed into two categories namely general AI and narrow AI. Based on learning methods, Machine learning can be distributed into three categories namely supervised learning, unsupervised learning, and reinforcement learning.
- The objective of artificial intelligence to develop smart systems like humans that can solve complex problems. The goal of machine learning is to let a machine learn from massive datasets so that they can provide accurate output.
- The key applications of artificial intelligence include customer support using chatbots, Siri, intelligent humanoid robot etc. The key applications of machine learning include Google search algorithms, online recommender system, Facebook friend tagging suggestions etc.
- Artificial intelligence holds a broad range of scope while machine learning comes with a limited scope.
- Artificial intelligence deals with structured, unstructured, and semi-structured data while machine learning works with only structured, and semi-structured data.
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Both machine learning and artificial intelligence can leave valuable business implications. In the context of the coming future, both are imperative to our society. A robust understanding of both of these fields will be extremely important to comprehend the rapidly changing business world and how the devices we use everyday work. The promises and value of both these fields are being materialized because of each other.
If you’re an aspiring candidate looking to step into these fields, this is probably the best time to begin your journey. As advancements and adoptions of both artificial intelligence and machine learning continue to accelerate, one thing can be assumed for sure — the impact will be profound.
What are the main differences between artificial intelligence and machine learning? was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
Getting Started with Feature Selection
For machine learning, more data is always better. What about more features of data? Not necessarily. This beginners’ guide with code examples for selecting the most useful features from your data will jump start you toward developing the most effective and efficient learning models.
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Data Science Tools Illustrated Study Guides
These data science tools illustrated guides are broken up into four distinct categories: data retrieval, data manipulation, data visualization, and engineering tips. Both online and PDF versions of these guides are available.
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Predictive Analytics World Berlin 2020 Keynote Sessions Announced
The agenda of Predictive Analytics World Berlin, 16-17 Nov, is taking shape. Curious? Take a peek at our keynote sessions 2020. Don’t forget to use the code KDNUGGETS for a 15% discount on your Predictive Analytics World ticket.
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Why Brands Need Cultural Intelligence
What is Cultural AI? How can an enhanced comprehension through the lens of culture and artificial intelligence allow marketers to fully optimize promotional spend and tailor marketing messages for better ROI?
As marketing becomes more hyper-personalized, enterprises stand to benefit from advancements in artificial intelligence (AI) that enable them to better understand their audiences, and drastically improve and customize multi-channel consumer messaging for diverse consumer audiences.
These we some of the topics discussed on July 22 as part of Cultural AI and Brands with moderators Davar Ardalan of IVOW AI and Swathi Young of Integrity Management Systems. Watch below to learn more about this emerging trend and what enterprises can gain from cultural context as they engage with consumers across AI products and solutions.
Panelists included Andy LaMora of Topcoder, Jeanne Lim of Nama Institute, Mark Germishuys of the data company NGA, and Shaun Chavis of LVNGbook.
Emerging themes centered on the importance of culturally-inclusive artificially intelligent systems to pave the way for a smarter and healthier future for both brands and consumers.
Background on the Cultural AI and Brands Speakers:
Davar Ardalan is the Founder of IVOW AI, bringing cultural intelligence to chatbots. Realizing that there is a gaping hole in AI algorithms that will define our future engagements, Davar created IVOW, to contextualize culture for brands. Prior to this, Ardalan was Deputy Director of the White House Presidential Innovation Fellowship Program in Washington, D.C. and a journalist for two decades at NPR News. As the Managing Editor of Hanson Robotics, Davar helped build Sophia the Robot’s AI. Ardalan has been featured in Forbes and AI Trends and has been recognized with a 2017 NASA Team Leadership award, a Gracie Award from the American Women in Radio and Television and a shout-out in the popular comic strip Zippy. In May 2014, she was the recipient of a United States Ellis Island Medal of Honor, for individual achievement and for promoting cultural unity.
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Swathi Young is the Chief Technology Officer at Integrity Management Services, Inc. In her more than two decades of technology experience, she has led over 100 projects globally — in Belgium, India, and the US — across e-commerce, supply chain, Hi-tech and financial sectors. She is a Women in AI Ambassador and founder of DC Emerging Technologies, a 2000+ member community that brings business leaders together to discuss applications of AI. Swathi recently co-authored the AI Playbook, a framework of implementing AI solutions for the US federal government.
Jeanne Lim is the founder and CEO of Nama Institute which advocates a conscious approach to living, leading, and innovating. Jeanne a board member and former CEO of Hanson Robotics, and previous to that the CMO and co-character lead of Sophia the Robot. She is also a board member of Kami Intelligence, an AI company developing a conversational AI platform that powers customer service, advisory, and educational solutions. Jeanne has over 25 years of marketing experience in the technology industry, as the Asia Pacific marketing director for Dell, Cisco, 3Com, and the corporate marketing director for a Silicon Valley-funded internet startup. She was the product and business manager for Apple in Asia Pacific, responsible for the launch of many of Apple’s groundbreaking products in the region.
Shaun Chavis is the founder and CEO of LVNGbook, a health engagement company in Atlanta that creates solutions which combine nutrition, culture, technology, and behavioral science. Shaun’s career includes 20 years as a journalist and media professional for organizations such as Time Inc. and ABC News / The Walt Disney Company. Her work has won two Luce Awards (Time Inc.’s highest honor), a James Beard Award, and made the New York Times Nonfiction Bestsellers list. She also co-founded FoodBlogSouth, which became the largest food blogging conference in the Southeast US. She has a culinary degree and a Master of Liberal Arts in Gastronomy from Boston University, and has taught in Boston University’s avocational cooking program and at Le Cordon Bleu College of Culinary Arts in Atlanta.
Mark Germishuys currently is CEO of NGA a company based in South Africa. Mark has received his fellowship, as a Chartered Certified Accountant(UK). Mark has 15 years’ experience in data and statistical pattern recognitions to identify valuable business insight from large data sets. With special focus on unsupervised and supervised modeling of natural language text. Mainly this entails the extraction of patterns from large text. Of special interest is the application of topic modeling and other text mining techniques to both short and long texts and understanding the modeling challenges posed by doing this. Mark has 20 years’ experience in the field of decision support models for clients using data. Of special focus is the various patents Mark has filed pertaining to better understanding big data and gathering techniques to make more informed decisions.
Andy LaMora is currently Wipro’s Global Director for Crowd Analytics & AI at Topcoder. In this capacity Andy creates Topcoder’s Analytics products and service offerings and contributes to corporate strategy. In previous roles Andy established and scaled Topcoder’s public sector practice, including long-term engagements with the NASA Tournament Lab and COECI, the Department of Energy and affiliated labs, Harvard Business and Medical Schools, and GSIs serving the public sector. Having spent over a decade at Topcoder, Andy is one of its most experienced leaders and advises corporations on adopting crowd strategies to boost productivity and innovation. Outside of Topcoder, Andy serves on the board for the Yellowstone Ecological Research Center, which seeks to apply field sensor saturation and cloud analytics to empower precision ecology and adaptive decision making. He also plays lead alto in the Sunday Night Big Band.
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Why Brands Need Cultural Intelligence was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
Thoughts of a Possible Future Utopia
It is the act of over-thinking which makes humans special. That blanket statement does ignore the many examples of non-human consciousness…
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Top 10 Skills You Need For A High-Paying Machine Learning Career
If there is a way to silently revolutionize the industry, then we have to talk about Machine Learning. A concept that is changing the way things are working is also paving the way for a new career trajectory. Machine Learning experts are in great demand, and it is only going to increase with time. Let us give you some examples, Siri, Alexa, Voice Assistance, Automated Cars, and many others all have Machine Learning at the core. Because of the growing dependence on Machine Learning, we have seen companies going ahead with Machine Learning training.
Here is a quick snap at some of the eye-catching data:
- According to Gartner’s report, AI has shown 2.3 million jobs in Machine Learning would be available by the end of 2020.
- Indeed, another report shows that Machine Learning Engineering, Software Engineering, topped the list for the most sought-after skills.
- Companies like Apple, Google, and Amazon have invested millions of dollars in research on Machine Learning and developing its future projects.
All this piece of information indicates that Machine Learning is going to be the hottest selling skills. But to excel as a machine learning expert, you must consider upskilling yourself so that you can become proficient in using ML concepts. If you join any Machine Learning training or certification course, you will learn about skills that will help improve your proficiency as a Machine Learning expert.
Ten skills that you need for a high-paying Machine learning job
- Applied mathematics- Mathematics plays a key role in Machine Learning, and if you wish to excel as a machine learning expert, then you must know about the mathematical skills. Some of the key concepts that you need to master are linear algebra, probability, statistics, multivariate calculus, algorithm, and optimization.
- Neural Network Architecture– the second concept that one needs to excel to become a Machine Learning expert is a neural network. These are a predefined set of algorithms to implement a machine learning task. Overall, it helps the machines to perform like the human brain.
- Data Modelling and evaluation- The next skill in our list is that of data modeling and evaluation. It helps in handling large volumes of data and assessing how the final system will work.
- Natural Language Processing– Machine Learning works on Natural Language Processing; it ensures that the machine works just like a human brain and interprets the same. Some of the techniques that you need to master while learning ML are Word2vec, Sentiment Analysis, Summarization, Gensim, and NLTK.
- Reinforcement Learning- It plays a key role in comprehending the conspectus of AI and deep learning, which are an integral part of Machine Learning. If you are going for machine learning for a beginner’s program, you must know about reinforcement learning.
- Distributed Computing– As an ML engineer, you would be required to work on a large volume of data, hence having complete knowledge about distributed computing is a must.
- Unix– ML engineers working on cluster data and servers are on Linux. Though a few ML engineers work on Windows and Mac, it becomes imperative that one must know about Unix and Linux systems.
- C, C++, and Java- As a magician learning expert, you must know programming languages. To understand the complexity of data and prepare an algorithm for machine learning, one needs to know C, C++, and Java.
- Spark and Hadoop- The next skill that will help you fetch a high-paying machine learning job is Spark and Hadoop. It is a framework for large scale implementation of Machine Learning.
- Rapid Prototyping– Choosing the right method of learning is imperative. Hence, a Machine Learning engineer needs to have good prototyping skills.
These skill sets will put you in the league of the most skilled machine learning expert. Not only it helps you become a proficient ML engineer, but at the same time, you will get better pay. Upskilling oneself is the need of the hour. The more you start learning about such concepts, the better the prospects of growth in your career profile.
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A bonus tip that we would like to highlight here is that you need to work on your communication skills. It will help in the right presentation of information, and you would be able to make even a naïve client understand complex machine learning concepts.
Concluding thoughts-
Machine Learning is silently impacting all past of our life, and this is also leading to an increased demand for machine learning experts. If you are also willing to become an expert in this field, Global Tech Council is offering a machine learning certification program. As a part of this program, you will learn about all the concepts of machine learning, along with its application.
For more such updates and information on the machine learning training program, connect with the Global Tech Council today.
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Top 10 Skills You Need For A High-Paying Machine Learning Career was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
DeepMinds Three Pillars for Building Robust Machine Learning Systems
Specification Testing, Robust Training and Formal Verification are three elements that the AI powerhouse believe hold the essence of robust machine learning models.
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