BIG DATA WITH MAX WELLING: LIVING WITH AI


Living with AI

Max Welling, Research chair in Machine Learning at the University of Amsterdam and VP technologies Qualcomm Netherlands, talks about reschooling, automation, employment, jobs, cloud computing, edge computing, servers, connection, delay, energy, battery, internet of things.

Oliver Greiner

As AI and big data continue disrupting industries across the board, issues related to transparency will inevitably force discussions into what people should really expect from AI-powered data analytics. For younger generations and digital natives who’ve often been said to be liberal with personal information on the internet, it’s critical for organizations that use this data to clearly outline the scope within which such data will be used, lest legal issues arise from misuse of trust.

Sárika Zsuzsi Görög

In response to Volodya Kuznetsov

AI’s impact on marketing is growing, predicted to reach nearly $40 billion by 2025. Most CMOs are aware of AI, but many are still unsure and unaware of the magnitude of the benefits and how they can adopt AI to improve marketing.  

Volodya,

before marketers commit to and execute their AI strategy, they need to understand the opportunity and difference between data analytics, predictive analytics and AI machine learning.

Volodya Kuznetsov

AI’s impact on marketing is growing, predicted to reach nearly $40 billion by 2025. Most CMOs are aware of AI, but many are still unsure and unaware of the magnitude of the benefits and how they can adopt AI to improve marketing.  

Teresa Guerrero

Machine learning, data science and data analytics or scientist are emerging fields growing into various sub-fields helping companies to improve their efficiency and performance at certain stages during the operations and services. Hence, understanding these technologies is very important to realize their right use and benefits into various sectors.

Анета Владимирова

Robots that resemble humans are known as androids; however, many robots aren't built on the human model. Industrial robots, for example, are often designed to perform repetitive tasks that aren't facilitated by a human-like construction. A robot can be remotely controlled by a human operator, sometimes from a great distance. A telechir is a complex robot that is remotely controlled by a human operator for a telepresence system, which gives that individual the sense of being on location in a remote, dangerous or alien environment and the ability to interact with it.

Doriane Mateu Phạm

In response to Drahoslava

Deep learning uses a system of layers where input is processed and then the processed output is passed on as input to the next layer, functioning much like neurons in the human brain. Through this system of input and output processing, deep learning agents model abstract thought in data. Deep learning systems can functionally can be broken down into two major categories: retrieval and generative models.

In retrieval-based models, functionality relies on a previously collected database of responses; the AI software matches questions to answers. This is the simpler type of model and generates no new responses. Retrieval-based models work well within narrowly defined roles. In chatbot AI, they don't make grammatical errors unless those errors are in their database. However, this model is not capable of handling new questions and can tend to inconsistency when asked the same question in a semantically different way.

Drahoslava

Deep learning uses a system of layers where input is processed and then the processed output is passed on as input to the next layer, functioning much like neurons in the human brain. Through this system of input and output processing, deep learning agents model abstract thought in data. Deep learning systems can functionally can be broken down into two major categories: retrieval and generative models.

Gunnr Østergård

Despite these potential risks, there are few regulations governing the use AI tools, and where laws do exist, the typically pertain to AI only indirectly. For example, federal Fair Lending regulations require financial institutions to explain credit decisions to potential customers, which limit the extent to which lenders can use deep learning algorithms, which by their nature are typically opaque.

Svetlana Barbieri

While AI is critical for self-driving cars, the military, commerce, AI-driven SEO and gaming, it’s poised to make the most human impact in medicine and human behavior. Imagine the UN leveraging neural networks and deep learning to discover what helps some communities thrive and others fall behind. Those lessons can then be leveraged into community builders, city planners, grants and projects.

Chares Valentinianus Kavanaugh

I think the greatest advantage of AI is the automation of tasks that will free up employees to focus on strategic initiatives. On the other hand, I don’t think it will be as big as predicted. There are still too many tasks that need a human touch to make them successful. We’ll see great benefit from AI in the more mundane areas, but you’ll always need the human brain for some tasks.

future hacker

AI will enable us to interact with information as if we’re interacting with a knowledgeable individual. We won’t have to look at a screen to learn about anything, we can simply converse with AI. SIRI is already a reliable personal assistant when it comes to setting reminders, alarm clocks, sending texts, etc. AI will make it possible for us to do virtually anything with voice command.

Тихомир Безлов

The greatest benefit of AI — which is already emerging — is the elimination of repetitive tasks. From chat bots that can free up human staffers’ times to work on more complex issues, to scheduling AIs like x.ai that eliminate the need to schedule meetings, AI will ultimately help humans spend more time focusing on creative and high-mental-effort activities.

Juniper Womack

The amount of data that's typically involved, and its variety, can cause data management issues in areas including data quality, consistency and governance. Also, data silos can result from the use of different platforms and data stores in a big data architecture. In addition, integrating Hadoop, Spark and other big data tools into a cohesive architecture that meets an organization's big data analytics needs is a challenging proposition for many IT and analytics teams, which have to identify the right mix of technologies and then put the pieces together.

Fujiko Nakayama

Big data analytics applications often include data from both internal systems and external sources, such as weather data or demographic data on consumers compiled by third-party information services providers. In addition, streaming analytics applications are becoming common in big data environments as users look to perform real-time analytics on data fed into Hadoop systems through stream processing engines, such as Spark, Flink and Storm.

Branka Pooja Addison

In response to Maria Arlotto Duchamps

Neural networks are a class of machine learning algorithms. The neuron part of neural is the computational component and the network part is how the neurons are connected. Neural networks pass data among themselves, gathering more and more meaning as the data moves along. Because the networks are interconnected, more complex data can be processed more easily. 

Deep learning is a subset of machine learning. It refers to using multi-layered neural networks to process data in increasingly complex ways, enabling the software to train itself to perform tasks like speech and image recognition through exposure to these vast amounts of data, for continual improvement in the ability to recognize and process information. Layers of neural networks stacked on top of each for use in deep learning are called deep neural networks.

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