BUSINESS


Mark Cuban: Invest in AI or Get Left Behind

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About 4,000 people listened to Cuban as he kicked off his shoes--literally--and explained how AI will change the game for companies, educators, and future developments. He's also keeping his eyes peeled for smaller companies in machine learning and AI, and already has at least three companies in his investment portfolio. "[Software writing] skill sets won't be nearly as valuable as being able to take a liberal arts education … and applying those [skills] in assisting and developing networks." But in order for the country to advance to that future, AI and robotics need to become core competencies in the U.S., and not just in the business world, Cuban said.


Why Artificial Intelligence and Machine Learning?

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Artificial intelligence and machine learning are helping people and businesses achieve key goals, obtain actionable insights, drive critical decisions, and create exciting, new, and innovative products and services. This definitely applies to Artificial Intelligence and Machine Learning as well, and so understanding and describing why these fields should be used for a given need is critical, and then should be followed by how they're used (e.g., processes, algorithms, data scientists), and lastly by what is produced as a result (e.g., product, service, recommendation engine, smart assistant). These days there are many amazing, real-world applications of artificial intelligence and machine learning being deployed to benefit both customers and companies. To learn more about artificial intelligence and machine learning, including driving goals, definitions, types, algorithms, processes involved, important tradeoffs and considerations, and examples of real-world applications for each category, check out my Goal-Driven Artificial Intelligence and Machine Learning class on Skillshare!


When AI (Artificial Intelligence) Goes Wrong...

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Many automated systems perform poorly, to the point that you are wondering if AI is an abbreviation for Artificial Innumeracy. Critical systems - automated piloting, running a power plant - usually do well with AI and automation, as considerable testing is done before deploying these systems. But for many mundane tasks, such as spam detection, chatbots, spell checking, detecting duplicate or fake accounts on social networks, detecting fake reviews or hate speech in social networks, search engine technology (Google) or AI-based advertising, a lot of progress must be made. For instance, if advertising dollars are misused by some poorly designed AI system (assuming the advertising budget is fixed) the negative impact on the business is limited.


Artificial or not, intelligence requires cleaned and mastered data

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Machine learning, for instance, is used in facial recognition, speech recognition, object recognition, and translation projects, with ramifications across many global industries, and deep learning can approach the level of neural networks to advance those abilities. With object recognition software, for instance, the ultimate question to ask about its efficiency will be: does it have enough data sets to distinguish among types? In this context, the well-worn field of master data management (MDM) assumes a crucial role in the age of AI. In this framing, markets for second- and third-party data will thrive, and the beneficiaries will be the businesses that have intelligent MDM platforms to accumulate and store the data, allowing them to create a data field from which AI can silo data, rather AI having to break down siloed data.


When Artificial Intelligence and Social Media Marketing Collide

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Both artificial intelligence and social media marketing are getting a lot of attention nowadays because of their huge benefits and growth potential. This feature can be used in various ways by the brands for developing their social media marketing strategies to further increase the reach and success of their social media marketing campaign. There are many creative social media marketers who are awesome at creating awesome contents. The various AI tools help them to collect the valuable insights from the data collected through various social media platforms to get incredible insights on the customer taste and preferences.


Domino habits for data science

@machinelearnbot

Inculcating discipline [Understanding business justification] – Explore and document'why' your data is there? What are the technical systems / business processes that generated this data? So go on bring out the learning, machine learning, deep learning packages and enjoy.. Inculcating discipline [Understanding business justification] – Explore and document'why' your data is there? So go on bring out the learning, machine learning, deep learning packages and enjoy..


Digital Marketing Trends for 2017 - Smart Insights Digital Marketing Advice

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Machine learning techniques apply across many of the techniques we discuss in this post including Big Data, Marketing Automation, Organic Search and Social media marketing. In our Digital Channel Essentials Toolkits within our members' area and our Digital Marketing Skills report we simplify digital marketing down to just 8 key techniques which are essential for businesses to manage today AND for individual marketers to develop skills. As defined in our question, Big Data marketing applications include market and customer insight and predictive analytics. Our social media research statistics summary shows continued growth in social media usage overall, but with reduced popularity of some social networks in some countries.


Why AI success depends on IT picking the low-hanging fruit

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Speaking to Computer Weekly about the results of the study, Capgemini global CTO Ron Tolido said: "In the short and mid-term, the benefits of AI are based on using off-the-shelf products to apply AI for a specific purpose, then training the AI to make it work." One example cited in the report is investment bank JP Morgan, whose lawyers spend thousands of hours studying financial deals. Among the areas Tolida expects to benefit from AI augmentation are customer service and the EU's General Data Protection Regulation (GDPR). "At Capgemini, we use AI for service management, cyber security, development and testing," he added.


Putting the "Science" Back in Data Science

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If you start your data analysis by simply stating hypotheses and applying Machine Learning algorithms, this is the wrong way. In a few words, I studied the past 27 years of Business Management literature and I tried to develop an epistemologically disruptive approach to measure and predict service quality, mixing Business Administration with Electrical Engineering concepts. Ah, profit call be predicted using Deep Neural Networks using data from Market Research, Financial Data and word embeddings from Social Media as features! So, the SCIENCE in Data Science is not only about Machine Learning, Deep Learning, Natural Language Processing, A.I.


3 machine learning success stories: An inside look

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Fewer technologies are hotter than artificial intelligence (AI) and machine learning (ML), which mimic the behavior of the human mind to help companies improve business operations. Even Uber, weathering several legal challenges, has made time to reveal Michelangelo, an internal ML-as-a-service platform, that "democratizes machine learning and makes scaling AI to meet the needs of business as easy as requesting a ride." For the past several months, he has been using Salesforce.com's Einstein AI/ML technology to increase personalization across the bank's small business, wholesale, commercial wealth and commercial banking units. Key advice: Using ML to identify patterns is the key to creating self-healing capabilities.