These are exciting times for theartificial intelligencecommunity.
I can tell that from my inbox.
A few years ago, I might have opened and read these emails with interest.
For the most part, I dont regret ignoring them.
This is mainly a guide for the PR people who are writing AI pitches.
These days, AI is mostly aboutmachine learninganddeep learning.
But theres a lot more to the field.
You must know aboutsymbolic AI, probabilistic programming,hybrid systems, and a lot more.
Alternatively, you could acquaint yourself withmachine learning concepts with Microsoft Excel.
AI research is what you see at conferences such as NeurIPS, ICLR, and CVPR.
AI research eventually finds its way into applications, but it takes time.
Pitching AI research is not very hard, given the paper presents a genuine idea.
Starting with the subject of your email, you should explicitly state that youre pitching a research paper.
This shows at first glance that your work has been verified and confirmed by experts in the field.
Note, however, that acceptance at a major conference of publication is not an absolute requirement.
In the past few years, Ive coveredseveralpapersthat were only published on arXiv without being presented at any conference.
Heres a decent subject line.
Theres a fundamental problem with this approach.
Want to make computer vision more secure?
Now, neuroscience offers an alternative: make the model more brain-like.
A final point to consider when sending AI research pitches is the people.
Behind every AI paper is a group of people coming from different backgrounds and with experience in different fields.
It might be their first time working together, or they might have worked on several joint projects before.
They might have other notable papers in the same field.
Mentioning these in the letter can solidify the pitch.
And thats because AI applications are not supposed to be exciting and cutting edge.
The key point in pitching AI products is to focus on the problem-solving aspect instead of the algorithms.
This approach results in vague and often erroneous language, which only causes confusion and disappointment.
Here are some bad AI subject lines Ive received recently because theyre too vague.
Talk with an expert?
But the sender could have written a better subject line that highlights FPGA programming challenges.
Heres a good subject line.
It focuses on the problem-solving aspect of the AI product.
Unfortunately, I didnt get to cover it (maybe in the future).
But dont forget the problem-solving aspect.
Heres a good subject line that mentions both the funding and the product.
Avoid vague language at all costs.
Heres a very bad pitch I received a while ago.
Ive redacted some parts to avoid shaming the company.
This is almost as bad as an AI pitch can get.
First, the pitch contains many inaccuracies.
The sender probably meant that the company uses basic machine learning algorithms (regression, decision trees, etc.
), deep learning, and other classic AI techniques, to solve problems in NLP and other domains.
The truth is that most real-world AI applications are boring.
They dont change a business overnight.
One more thing to avoid is building your pitch on top of sensitive issues.
Unless your technology is absolutely relevant to these kinds of issues, dont mention it.
An AI that can help detect fake news about the elections doesnt cut it.
Even though AI is automating many tasks, it is still the people who are creating the products.
you could read the original articlehere.
