This week, the benefits of peripheral vision for machines.
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Our team of experts have scoured the internet for the most exciting and informative articles so that you can stay up-to-date on all things digital, data, blockchain, AI & analytics.
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Implicit Intelligence and Learning New Cultures
Behind-the-scenes mechanisms of learning that happen outside awareness.
- Implicit intelligence is the ability to pick up complex patterns from the environment without trying to do so.
- For learning new cultures, implicit aptitude may be more important than explicit aptitude such as IQ.
- One of the best ways to activate our implicit mechanisms when learning new cultures is through exposure.
One of our lifelong love affairs as humans is learning. Even the “pain” which, as per Aristotle’s observations, accompanies learning doesn’t deter us. In the name of growing our minds and skillsets, we roll up our sleeves, sharpen our focus, read, move, watch, listen, persevere, and repeat. As the hours swirl by, the neurons in our brains rearrange, and then, suddenly, we are playing Beethoven, winning a game of chess, and skiing down a slope without tumbling.
Luminous debuted last week with $105 million Series A
A new startup thinks light is the key to building a next-gen supercomputer
Last Thursday, a buzzy new supercomputer startup debuted—with $105 million in Series A funding.
The startup’s ethos? Software has come a long way, but the hardware to support everything AI can do doesn’t exist yet. And Luminous Computing believes it has the solution: building the world’s most powerful supercomputer.
- Luminous’s investors include Bill Gates and Gigafund, the VC firm founded by PayPal and Founders Fund alumni.
Open Data and Why it is Necessary
Open data improves accessibility and encourages universal participation, which allows companies to create cutting-edge, data-driven technologies and make the world a better place.
What is Open Data?
Open data is the data that can be accessed by anyone for any purpose. It allows individuals or companies to use, reuse, and re-distribute data without any legal issues. It is subject to author attribution or sharealike – opendatahandbook.
To understand better let’s dive into the functions.
- Open Access and Availability: The data must be complete and can be easily downloadable via the internet. The data should also be available in a convenient and modified form.
- Open to re-use: The data must be under a license that allows end-users to re-use and re-distribute which also includes mixing of multiple datasets.
- Universal Participation: Everyone can use, reuse, and redistribute the data without discrimination against any field of study, individual or a group.
5 Product Management Tips for Data Science Projects
Keeping data science projects on the right trajectory can be a challenge for even the best manager.
Data science management has become an essential element for companies that want to gain a competitive advantage. The role of data science management is to put the data analytics process into a strategic context so that companies can harness the power of their data while working on their data science project.
Data science management emphasizes aligning projects with business objectives and making teams accountable for results. It means ensuring that each team is in place, whether under the same office or as a distributed team. It also ensures that the team members are provided with appropriate roles and people contributing towards the project’s success.
How Legacy Companies Can Pivot to a Platform Model
Ensure that data comes with the relationship
Platform companies like Facebook, Amazon, Google, and Tencent have created value at stunning rates. They grow rapidly and own few assets — and they’ve all made strong use of AI.
What can legacy companies learn from these platforms? And is it possible for legacy companies to use this business model, too? Looking at legacy firms that have successfully done just that, companies should:
- Strategize about how ecosystem relationships will improve your offerings, and seek out those partnerships.
- Ensure that data comes with the relationship.
- Develop an API-based IT services architecture.
- Identify the key decisions that AI needs to make, and gather the data to train models.
- Design a seamless process from the customer’s standpoint.
- Use data from across the ecosystem to improve models and offerings.
Article by @MIT
Perhaps computer vision and human vision have more in common than meets the eye?
Research from MIT suggests that a certain type of robust computer-vision model perceives visual representations similar to the way humans do use peripheral vision. These models, known as adversarially robust models, are designed to overcome subtle bits of noise that have been added to image data
The way these models learn to transform images is similar to some elements involved in human peripheral processing, the researchers found. But because machines do not have a visual periphery, little work on computer vision models has focused on peripheral processing, says senior author Arturo Deza, a postdoc in the Center for Brains, Minds, and Machines.