This is an experiment.
We will prompt a model with a piece of text and assess how well it understands the contents.
The model sits behind your eyes.
The data used to train this model is your entire life, up to and including this very moment.
Continue readingTransformers are a very popular architecture in machine learning. While they were first introduced in natural language processing, they have been applied to many fields such as protein folding and design.
Transformers were first introduced in the excellent paper Attention is all you need by Vaswani et al. The paper describes the key elements, including multiheaded attention, and how they come together to create a sequence to sequence model for language translation. The key advance in Attention is all you need is the replacement of all recurrent layers with pure attention + fully connected blocks. Attention is very efficeint to compute and allows for fast comparisons over long distances within a sequence.
One issue, however, is that attention does not natively include a notion of position within a sequence. This means that all tokens could be scrambled and would produce the same result. To overcome this, one can explicitely add a positional encoding to each token. Ideally, such a positional encoding should reflect the relative distance between tokens when computing the query/key comparison such that closer tokens are attended to more than futher tokens. In Attention is all you need, Vaswani et al. propose the slightly mysterious sinusoidal positional encodings which are simply added to the token embeddings:
Last month, a drift of OPIGlets attended the royal society of chemistry’s annual AI in chemistry conference. Co-organised by the group’s very own Garrett Morris and hosted in Churchill College, Cambridge, during a heatwave (!), the two days of conference featured aspects of artificial intelligence and deep machine learning methods to applications in chemistry. The programme included a mixture of keynote talks, panel discussion, oral presentations, flash presentations, posters and opportunities for open debate, networking and discussion amongst participants from academia and industry alike.
Continue readingAt OPIG Group Meetings, it’s customary to give “Conference Feedback” whenever any of us has recently attended a conference. Typically, people highlight the most interesting talks—either to them or others in the group.
Having just returned from the 6th RSC-BMCS / RSC-CICAG AI in Chemistry Symposium, it was my turn last week. But instead of the usual perspective—of an attendee—I spoke briefly about how to organize a conference.
Continue readingMultivariate Normal distributions are an essential component of virtually any modern deep learning method—be it to initialise the weights and biases of a neural network, perform variational inference in a probabilistic model, or provide a tractable noise distribution for generative modelling.
What most of us (including—until very recently—me) aren’t aware of, however, is that these Normal distributions begin to look less and less like the characteristic bell curve that we associate them with as their dimensionality increases.
Continue readingRecently, I’ve been using a Convolutional Neural Network (CNN), and other methods, to predict the binding affinity of antibodies from their sequence. However, nine months ago, I applied a CNN to a far more important task – distinguishing images of butter from margarine. Please check out the GitHub link below to learn moo-re.
https://github.com/lewis-chinery/AI_cant_believe_its_not_butter
We’ve been investigating deep learning-based protein-ligand docking methods which often claim to be able to generate ligand binding modes within 2Å RMSD of the experimental one. We found, however, this simple criterion can conceal a multitude of chemical and structural sins…
DeepDock attempted to generate the ligand binding mode from PDB ID 1t9b
(light blue carbons, left), but gave pretzeled rings instead (white carbons, right).
Lucubration – The action or occupation of intensive study originally by candle or lamplight.
Gaslighting – Psychological abuse in which a person or group causes someone to question their own sanity, memories, or perception.
I was recently having a play with Google Bard. Bard, unlike ChatGPT has access to live data. It also undergoes live feedback and quality control. I was hoping to see if it would find me any journals with articles on prion research which I’d previously overlooked.
Me: Please show me some recent articles about prion research.
(Because always be polite to our AI overlords, they’ll remember!)
OPIG’s growing immunoinformatics team continues to develop and openly distribute a wide variety of databases and software packages for antibody/nanobody/T-cell receptor analysis. Below is a summary of all the latest updates (follows on from v1.0 and v2.0).
Continue readingIn my previous blog post, I explored the capabilities of ChatGPT 3.5, testing its skills as a programmer and mathematician’s assistant. The results were mixed, to say the least. While it could handle simple coding tasks with ease, it faltered when faced with more complex mathematical problems and image manipulation tasks. I concluded that while ChatGPT 3.5 was impressive, it was far from replacing us in our jobs. It was a tool, yes, but one that needed careful supervision and frequent corrections.
Fast forward to today, and we have ChatGPT 4.0 with code interpreter enabled. I approached this new version with the same scepticism I had for its predecessor, but I must admit, my initial impressions have been challenged. The leap from 3.5 to 4.0 is not just a step forward; it’s a giant leap. You can see my experiment here https://chat.openai.com/share/0099a12f-e577-44bd-aa10-7c7508f15f19.
In my previous experiment, I asked ChatGPT 3.5 to translate a 2D image, a task that combines both programming and a bit of maths. The results were less than satisfactory. It was able to write code to translate images if they were in real space, a task that can be easily found searching on the web. However, it struggled to handle images represented in Fourier space correctly, and even with multiple hints, the best it could do was a rough approximation of the correct solution. It took six attempts to get the code automatically generated, and even then, it was far from perfect.
With ChatGPT 4.0, however, the story is different. When asked to perform a similar task, it provided a clear, concise, and correct solution. It understood the concept of image translation in the context of computer vision and image processing, and it was able to generate Python code using the scikit-image package to perform the task. The code was not only syntactically correct but also logically sound, demonstrating a significant improvement in understanding and problem-solving capabilities.
"""
#Me: Can you know provide some python code to translate (pixel shifts) images? And can you execute it?
#Chat gpt: Here's a simple Python example using scikit-image to translate an image:
"""
import numpy as np
import matplotlib.pyplot as plt
from skimage import data
from skimage.transform import AffineTransform, warp
# Load an example image
image = data.camera()
# Define a translation transform
tform = AffineTransform(translation=(50, 50))
# Apply the transform to the image
translated_image = warp(image, tform.inverse)
# Plot the original and translated images
fig, ax = plt.subplots(1, 2)
ax[0].imshow(image, cmap='gray')
ax[0].set_title('Original Image')
ax[1].imshow(translated_image, cmap='gray')
ax[1].set_title('Translated Image')
plt.show()
"""
#Chat gpt:
In this code, AffineTransform is used to define a translation of 50 pixels to the right and 50 pixels down, and warp is used to apply this transform to the image. The result is an image that has been shifted.
However, I must clarify that the current environment doesn't support displaying plots, so the matplotlib part of the code won't work here. You should be able to run it in a Jupyter notebook or similar environment on your local machine. Let me know if you need further help or clarification!
"""
"""
ME: perform the same operation as before but with an image represented in Fourier space.
#Chat gpt:
Sure! However, we need to keep in mind that translating an image in the Fourier domain is not the same as translating an image in the spatial domain. In the Fourier domain, translations are performed by multiplying the image by a complex exponential, which essentially changes the phase of the Fourier transform.
Here is a Python code snippet demonstrating translation in the Fourier domain:
"""
import numpy as np
from skimage import data
from scipy.fft import fft2, ifft2, fftshift, ifftshift
# Load an example image
image = data.camera()
# Compute the 2-dimensional FFT of the image
image_fft = fft2(image)
# Define a translation
dy, dx = 30, 30
# Generate an array with the same size as the image
x = np.arange(0, image.shape[1])
y = np.arange(0, image.shape[0])
xv, yv = np.meshgrid(x, y)
# Create the translation function in Fourier space
translation = np.exp(-2j * np.pi * (dx * xv / image.shape[1] + dy * yv / image.shape[0]))
# Translate the image in Fourier space
translated_image_fft = image_fft * translation
# Transform back to spatial domain
translated_image = np.abs(ifft2(translated_image_fft))
# Now you could plot the images
"""
#Chat gpt:
This code first computes the Fourier transform of the image using fft2. Then it generates a complex exponential function that represents a shift in the Fourier domain. This function is multiplied with the Fourier-transformed image to achieve the translation. Finally, the inverse Fourier transform ifft2 is used to transform the image back to the spatial domain.
As before, the current environment doesn't support displaying plots, so you would need to run this on your local machine to see the translated image.
"""
Another test I revisited was a mathematical problem I had posed to ChatGPT 3.5: “What is the intersection between the unit circle and the curve y = x^2?” The previous version had failed to provide a correct answer. However, ChatGPT 4.0 not only understood the problem but also wrote its own code to solve it, correctly identifying the intersection points. This was a clear demonstration of the AI’s improved mathematical problem-solving capabilities although It’s important to note that only the premium version of ChatGPT 4.0 can run the code it suggests, as it did in the example of the intersection of the unit circle and the parabola. +
So, is my job in danger now? Well, it’s a complex question. The advancements in AI, as demonstrated by the jump from ChatGPT 3.5 to 4.0, are indeed impressive. The AI’s ability to understand complex tasks and generate accurate solutions is growing quite fast. However, it’s important to remember that AI, at its core, is a tool. It’s a tool that can augment our capabilities, automate mundane tasks, and help us solve complex problems. In the end, whether AI becomes a threat or an ally in our jobs depends largely on how we choose to use it. If we see it as a tool to enhance our skills and productivity, then there’s no danger, only opportunity. But if we see it as a replacement for human intelligence and creativity, then we might indeed have cause for concern. For now, though, I believe we’re safe. The Turing test might be a thing of the past, but the “human test” is still very much alive.