Robohub.org
 

Why we need journalism about machine learning


by
25 September 2015



share this:

Talking Machines

Talking Machines is in the process of raising funds to defray the cost of producing our first season and to help us start production on our second season. On the show we’ve talked about how we’ll use the money (to pay for studio time, editing, and the cost of travel to get our great interviews). But we haven’t gotten to the heart of the question yet: Do we even need journalism about machine learning?

We need journalism about machine learning, artificial intelligence, and data science desperately. Not just to calm the public conversation, which always seems to be full of hype on these topics, but to make sure that work in our field is sustainable. And no one is going to make the case for our industry unless we do it ourselves.

I live in Cambridge, MA. A lot of the people here are scientists or are training to enter the field. From the vantage point of Cambridge, the answer seems to be a resounding yes, we do need journalism about these topics, and Talking Machines is a way for those in the field to access each other’s ideas, and for those in training to get exposure to work they might not have heard of.

But not all of our listeners live in Cambridge, or come from an academic background. We get letters from all over the world saying that Talking Machines has allowed them to better understand ideas they’d like to use in their business, helped them talk with their data teams, or helped them make the right hire.

Most importantly though, not all of our listeners think they live a life that has anything to do with machine learning, or computer science .. or science at all. We started Talking Machines because we wanted to open the world of machine learning up to a wider audience, to help them understand the reality of research in the field and the industry, and how that impacts their lives in a real way on a daily basis.

The public conversation around machine learning (and by extension artificial intelligence) is filled with extreme hype, both positive and negative. These extremes have lead to a crippling pattern of “winters” where interest, activity, and funding in the field dries up. If we present the reality of what is happening in the field in a way that invites the public to be part of the conversation, that arms them with the knowledge they need to participate, and we will create a more sustainable industry for ourselves. For our own benefit, and for the good of those who use the tools we make, it’s our responsibility to play a bigger role than we have before in the public conversation.

Talking Machines does just that. By introducing machine learning to a wide audience in a way that allows people in, we ensure realistic expectations of work coming out of both in the industry and the field. More than that, we allow people to understand the tools that they use every day and the impact that they have. It is our responsibility to make sure we are accurately represented, and only we can do that. Our project has been going on for a little under a year now, and we’ve made a difference in the accessibility of the field.

But if we’re going to keep going, we need your help to do so.

Support Talking Machines’ Kickstarter campaign to keep journalism on machine learning going strong! 

 



tags: , , , , ,


Talking Machines is your window into the world of machine learning.
Talking Machines is your window into the world of machine learning.





Related posts :



Robot see, robot do: System learns after watching how-tos

  14 May 2025
Researchers have developed a new robotic framework that allows robots to learn tasks by watching a how-to video

AI-powered robots help tackle Europe’s growing e-waste problem

  12 May 2025
EU-funded researchers have developed adaptable robots that could transform the way we recycle electronic waste, benefiting both the environment and the economy.

Robot Talk Episode 120 – Evolving robots to explore other planets, with Emma Hart

  09 May 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Emma Hart from Edinburgh Napier University about algorithms that 'evolve' better robot designs and control systems.

Robot Talk Episode 119 – Robotics for small manufacturers, with Will Kinghorn

  02 May 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Will Kinghorn from Made Smarter about how to increase adoption of new tech by small manufacturers.

Multi-agent path finding in continuous environments

  01 May 2025
How can a group of agents minimise their journey length whilst avoiding collisions?

Interview with Yuki Mitsufuji: Improving AI image generation

  29 Apr 2025
Find out about two pieces of research tackling different aspects of image generation.

Robot Talk Episode 118 – Soft robotics and electronic skin, with Miranda Lowther

  25 Apr 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Miranda Lowther from the University of Bristol about soft, sensitive electronic skin for prosthetic limbs.

Interview with Amina Mević: Machine learning applied to semiconductor manufacturing

  17 Apr 2025
Find out how Amina is using machine learning to develop an explainable multi-output virtual metrology system.



 

Robohub is supported by:




Would you like to learn how to tell impactful stories about your robot or AI system?


scicomm
training the next generation of science communicators in robotics & AI


©2025.05 - Association for the Understanding of Artificial Intelligence


 












©2025.05 - Association for the Understanding of Artificial Intelligence