text
stringlengths 301
426
| source
stringclasses 3
values | __index_level_0__
int64 0
404k
|
|---|---|---|
Deep Learning, Artificial Intelligence, Convolutional Network, Image Classification, Object Detection.
Resizer] [Vision Transformer, ViT] [ResNet Strikes Back] [DeiT] [EfficientNetV2] [MLP-Mixer] [T2T-ViT] [Swin Transformer] [CaiT] [ResMLP] [ResNet-RS] [NFNet] [PVT, PVTv1] [CvT] [HaloNet] [TNT] [CoAtNet] [Focal Transformer] [TResNet] [CPVT] [Twins] 2022 [ConvNeXt] [PVTv2] My Other Previous Paper
|
medium
| 5,952 |
Science, Protein Engineering, Molecular Biology, Biochemistry, Virtual Reality.
Learn more today: bit.ly/NanomeAI 23andMe Inc, Codexis, GE Healthcare, 3M Co, Amgen, Bayer, Bristol Myers Squibb, Eli Lilly & Co, Merck, Novo Nordisk, and Pfizer. The big names were there at the Cambridge Healthtech Institute’s 18th Annual PepTalk, and so was Nanome. The event was held in Nanome’s
|
medium
| 5,954 |
Science, Protein Engineering, Molecular Biology, Biochemistry, Virtual Reality.
backyard from last month at the Hilton San Diego Bayfront. (We’re based just a short drive away in La Jolla, California, on the UC San Diego campus) We were thrilled to attend one of the largest annual gatherings of protein science researchers in the world. This year, PepTalk hosted nearly 1,500
|
medium
| 5,955 |
Science, Protein Engineering, Molecular Biology, Biochemistry, Virtual Reality.
participants comprised of leading thinkers from biotech, pharma, and academia. Nanome Booth at PepTalk Many new announcements were made at PepTalk. For instance, clinical-stage immunotherapy company Vaccinex, Inc. (Nasdaq: VCNX) Vice President of Preclinical Research, Elizabeth Evans, Ph.D.,
|
medium
| 5,956 |
Science, Protein Engineering, Molecular Biology, Biochemistry, Virtual Reality.
presented on the company’s anti-SEMA4D research and data from its Phase 2 trial in Huntington’s Disease. In her presentation, “An Emerging Role for Glial Cells and Guidance Molecules in Neurodegeneration,” Dr. Evans’s explained the pathogenic role that the SEMA4D protein plays in neurodegeneration.
|
medium
| 5,957 |
Science, Protein Engineering, Molecular Biology, Biochemistry, Virtual Reality.
She noted her company’s experience with its monoclonal antibody, VX15 (pepinemab), in blocking the molecule. Chief Experience Officer Edgardo and Alessandro Monge of the Nanome Team Spheryx, Inc. introduced its new particle characterization instrument, xSight, at PepTalk. xSight implements
|
medium
| 5,958 |
Science, Protein Engineering, Molecular Biology, Biochemistry, Virtual Reality.
Spheryx’s proprietary technology, Total Holographic Characterization®, which is used for detecting, counting and characterizing sub-visible particles in complex heterogeneous combinations. These might include protein aggregates, silicone oil droplets, and other contaminants in biologic
|
medium
| 5,959 |
Science, Protein Engineering, Molecular Biology, Biochemistry, Virtual Reality.
formulations. Dr. Laura Philips, Spheryx President and CEO, presented “Detecting, Counting and Characterizing Sub-Visible Protein Aggregates with Holographic Video Microscopy”, and reported recent findings on characterizing protein aggregates in the presence of silicone oil. http://bit.ly/Nanomeinc
|
medium
| 5,960 |
Science, Protein Engineering, Molecular Biology, Biochemistry, Virtual Reality.
GenScript announced at PepTalk the launch of its AmMag™ SA semi-automatic purification instrument, which accelerates and simplifies protein and antibody purification. Instead of taking days, this process now takes mere hours. Developed in collaboration with Amgen, the technology integrates the
|
medium
| 5,961 |
Science, Protein Engineering, Molecular Biology, Biochemistry, Virtual Reality.
speed and high binding capacity found in magnetic beads with high throughput for larger sample volumes. “Traditional purification instruments operate at a very low speed. Their throughput is limited to the ‘one filtered sample at a time’ mode of operation, rendering purification a painstakingly
|
medium
| 5,962 |
Science, Protein Engineering, Molecular Biology, Biochemistry, Virtual Reality.
slow process,” said GenScript Marketing Director Roumen Bogoev. “The AmMag SA purification instrument produces similar results to traditional methods at a fraction of the time, offering organizations a new tool for accelerating the drug development process and ultimately bringing life-saving
|
medium
| 5,963 |
Science, Protein Engineering, Molecular Biology, Biochemistry, Virtual Reality.
medications to market more quickly.” Dr. Jan Jezek, Chief Scientific Officer for UK-based leading formulation tech company Arecor Limited, presented “Novel Biopharmaceutical Compositions to Reduce the Rate of Aggregation”. Dr Jezek’s PEPTalk presentation summarized key principles behind its
|
medium
| 5,964 |
Science, Protein Engineering, Molecular Biology, Biochemistry, Virtual Reality.
proprietary formulation technology, Arestat. The tech enables biopharmaceutical products that keep protein aggregation to a minimum so scientists can create safe and convenient products not achievable using conventional formulation methods. According to Arecor, one benefit might be products that do
|
medium
| 5,965 |
Science, Protein Engineering, Molecular Biology, Biochemistry, Virtual Reality.
not need to be refrigerated. In “Driving Efficiency in Purification with Automated Multistep and Parallel Chromatography”, Hoang Tran, Senior Field Applications Scientist at GE Healthcare Life Sciences, spoke about how scientists are improving upon the traditional single-step purification
|
medium
| 5,966 |
Science, Protein Engineering, Molecular Biology, Biochemistry, Virtual Reality.
methodology with more efficient and automated parallel and continuous processing. Dr. Peter Schmidt, Director of Recombinant Technology Research at CSL Behring, discussed Monoclonal antibodies, which he called “the fastest growing segment in the drug market.” He discussed how mABs development
|
medium
| 5,967 |
Science, Protein Engineering, Molecular Biology, Biochemistry, Virtual Reality.
requires purifications of a great number of variants that have also sufficient yield. High-throughput purification strategies are limited, Dr. Schmidt argued, because the binding capacity of established affinity matrices is too low. The CSL Behring Director revealed that his company’s results
|
medium
| 5,968 |
Science, Protein Engineering, Molecular Biology, Biochemistry, Virtual Reality.
showed that matrices developed for continuous chromatography applications increase the yield in high-throughput and lab-scale antibody purification. In the talk “Establishing Innovative and Efficient Tool Boxes for Optimal and Scalable Processes for Recombinant Proteins,” Yuyi Shen, phD, Associate
|
medium
| 5,969 |
Science, Protein Engineering, Molecular Biology, Biochemistry, Virtual Reality.
Director of Process Development & Manufacturing at Bolt Biotherapeutics, discussed how technology advances bioprocessing and manufacturing in the pharmaceutical industry. Dr. Shen showed the successful implementation of tools for process development and improvement for mAbs and complex recombinant
|
medium
| 5,970 |
Science, Protein Engineering, Molecular Biology, Biochemistry, Virtual Reality.
proteins. Overall, PepTalk was a great experience. It was a pleasure having so many thought leaders in our own backyard. We were busy manning the booth, but did catch some very interesting talks that reinforced our belief that technology like VR can revolutionize the pharmaceutical, chemical and
|
medium
| 5,971 |
Machine Learning, Data Science, Technology, Python, Data Engineering.
Faster big-data analysis workflows with an open-source library If you’re a data scientist working with large datasets, you must have run out of memory (OOM) when performing analytics or training machine learning models. Supercharge your workflow. Photo by Cara Fuller on Unsplash That’s not
|
medium
| 5,973 |
Machine Learning, Data Science, Technology, Python, Data Engineering.
surprising. The memory available on a desktop or laptop computer can easily exceed large datasets. We are forced to work with only a small subset of data at a time, which can lead to inefficient data analysis. Worse, performing data analysis on large datasets can take a long time, especially when
|
medium
| 5,974 |
Machine Learning, Data Science, Technology, Python, Data Engineering.
using complex algorithms and models. Disclaimer: I am not affiliated with vaex. Vaex: a data analysis library for large datasets. Enter vaex. It is a powerful open-source data analysis library for working with large datasets. It speeds up data analysis by working with large datasets that would not
|
medium
| 5,975 |
Machine Learning, Data Science, Technology, Python, Data Engineering.
fit in memory using an out-of-core approach. This means it only loads the data into memory as needed. Vaex: the silver bullet to large datasets (Source) 4 key features of vaex Some of the key features of vaex that make it useful for speeding up data analysis include: Fast and efficient handling of
|
medium
| 5,976 |
Machine Learning, Data Science, Technology, Python, Data Engineering.
large datasets: vaex uses an optimized in-memory data representation and parallelized algorithms. vaex works with huge tabular data, processes 1,000,000,000 rows/second. Flexible and interactive data exploration: it allows you to interactively explore data using a variety of built-in visualizations
|
medium
| 5,977 |
Machine Learning, Data Science, Technology, Python, Data Engineering.
and tools, including scatter plots, histograms, and kernel density estimates. Easy-to-use API: vaex has a user-friendly API. The library also integrates well with popular data science tools like pandas, numpy, and matplotlib. Scalability: vaex scales to very large datasets and can be used on a
|
medium
| 5,978 |
Machine Learning, Data Science, Technology, Python, Data Engineering.
single machine or distributed across a cluster of machines. Process data at lightning speed. Image by stable diffusion. Getting started with vaex To use Vaex in your data analysis project, you can simply install it using pip: pip install vaex Once Vaex is installed, you can import it into your
|
medium
| 5,979 |
Machine Learning, Data Science, Technology, Python, Data Engineering.
Python code and perform analytics. Here is a simple example of how to use Vaex to calculate the mean and standard deviation of a dataset. import vaex # load an example dataset df = vaex.example() # calculate the mean and standard deviation mean = df.mean(df.x) std = df.std(df.x) # print the results
|
medium
| 5,980 |
Machine Learning, Data Science, Technology, Python, Data Engineering.
print("mean:", mean) print("std:", std) The example dataframe (MIT license) has 330,000 rows. In this example, we use the vaex.open() function to load an example dataframe (screenshot above), and then use the mean() and std() methods to calculate the mean and standard deviation of the dataset. Vaex
|
medium
| 5,981 |
Machine Learning, Data Science, Technology, Python, Data Engineering.
syntax is similar to pandas Filtering with vaex Many functions in vaex are similar to pandas. For example, for filtering data with vaex, you can use the following. df_negative = df[df.x < 0] print(df_negative[['x','y','z','r']]) Grouping by with vaex Aggregating data is essential for any analytics.
|
medium
| 5,982 |
Machine Learning, Data Science, Technology, Python, Data Engineering.
We can use vaex to perform the same function as we do for pandas. # Create a categorical column that determines if x is positive or negative df['x_sign'] = df['x'] > 0 # Create an aggregation based on x_sign to get y's mean and z's min and max. df.groupby(by='x_sign').agg({'y': 'mean', 'z': ['min',
|
medium
| 5,983 |
Machine Learning, Data Science, Technology, Python, Data Engineering.
'max']}) Other aggregation, including count, first,std, var, nunique are available. Performing machine learning with vaex You can also use vaex to perform machine learning. Its API has very similar structure to that of scikit-learn. To use that we need to perform pip install. import vaex We will
|
medium
| 5,984 |
Machine Learning, Data Science, Technology, Python, Data Engineering.
illustrate how one can use vaex to predict the survivors of Titanic. Using titanic survivor problem to illustrate vaex. Image by Stable Diffusion. First, need to load the titanic dataset into a vaex dataframe. We will do that using the vaex.open() method, as shown below: import vaex # Download the
|
medium
| 5,985 |
Machine Learning, Data Science, Technology, Python, Data Engineering.
titanic dataframe (MIT License) from https://www.kaggle.com/c/titanic # Load the titanic dataset into a vaex dataframe df = vaex.open('titanic.csv') Once the dataset is loaded into the dataframe, we can then use vaex.mlto train and evaluate a machine learning model that predicts whether or not a
|
medium
| 5,986 |
Machine Learning, Data Science, Technology, Python, Data Engineering.
passenger survived the titanic disaster. For example, the data scientist could use a random forest classifier to train the model, as shown below. from vaex.ml.sklearn import Predictor from sklearn.ensemble import GradientBoostingClassifier # Download the titanic dataframe (MIT License) from
|
medium
| 5,987 |
Machine Learning, Data Science, Technology, Python, Data Engineering.
https://www.kaggle.com/c/titanic # Load the titanic dataset into a vaex dataframe titanic_df = vaex.open('titanic.csv') titanic_df = titanic_df.dropna() # Get numeric columns of titanic_df features = ['Age','SibSp','Parch','Fare','Pclass'] target = 'Survived' # Use GradientBoostingClassifier as an
|
medium
| 5,988 |
Machine Learning, Data Science, Technology, Python, Data Engineering.
example model = GradientBoostingClassifier(random_state=42) vaex_model = Predictor(features=features, target=target, model=model, prediction_name='prediction') vaex_model.fit(df=titanic_df) Of course, other preprocessing steps and machine learning models (including neural networks!) are available.
|
medium
| 5,989 |
Machine Learning, Data Science, Technology, Python, Data Engineering.
Once the model is trained, the data scientist perform prediction using thetransform() method, as shown below: titanic_df = vaex_model.transform(titanic_df) Let’s print the results. Notice there is a new column “prediction”. print(titanic_df) Results from the prediction Using vaex to solve the
|
medium
| 5,990 |
Machine Learning, Data Science, Technology, Python, Data Engineering.
titanic problem is an absolute overkill, but this serves to illustrate that vaex can solve machine learning problems. Use vaex to supercharge your data science pipelines Overall, vaex.ml provides is a powerful tool to perform machine learning on large datasets. Its out-of-core approach and
|
medium
| 5,991 |
Machine Learning, Data Science, Technology, Python, Data Engineering.
optimized algorithms make it possible to train and evaluate machine learning models on datasets that would not fit in memory. We didn’t cover many of the functions available to vaex. To do that, I strongly encourage you to look at the documentation. Here is the full code: import vaex # load an
|
medium
| 5,992 |
Machine Learning, Data Science, Technology, Python, Data Engineering.
example dataset df = vaex.example() print(df) # calculate the mean and standard deviation mean = df.mean(df.x) std = df.std(df.x) # print the results print("mean:", mean) print("std:", std) df_negative = df[df.x < 0] print(df_negative) # Create a categorical column that determines if x is positive
|
medium
| 5,993 |
Machine Learning, Data Science, Technology, Python, Data Engineering.
or negative df['x_sign'] = df['x'] > 0 # Create an aggregation based on x_sign to get y's mean and z's min and max. df.groupby(by='x_sign').agg({'y': 'mean', 'z': ['min', 'max']}) from vaex.ml.sklearn import Predictor from sklearn.ensemble import GradientBoostingClassifier # Download the titanic
|
medium
| 5,994 |
Machine Learning, Data Science, Technology, Python, Data Engineering.
dataframe (MIT License) from https://www.kaggle.com/c/titanic # Load the titanic dataset into a vaex dataframe titanic_df = vaex.open('titanic.csv') titanic_df = titanic_df.dropna() # Get numeric columns of titanic_df features = ['Age','SibSp','Parch','Fare','Pclass'] target = 'Survived' model =
|
medium
| 5,995 |
Machine Learning, Data Science, Technology, Python, Data Engineering.
GradientBoostingClassifier(random_state=42) vaex_model = Predictor(features=features, target=target, model=model, prediction_name='prediction') vaex_model.fit(df=titanic_df) titanic_df = vaex_model.transform(titanic_df)Follow me for more content. I am a data scientist working in tech. I share data
|
medium
| 5,996 |
Python, Linux, Linguistics, Authors.
Install Anaconda in Ubuntu & Windows10 (2019) Anaconda is the updated version of PYTHON.For AI, Machine learning,Deep learning and Digital Signal Processing we mostly use Anaconda. Basic steps involved in installation:- Go to google and type anaconda install click on the links on google / i am
|
medium
| 5,998 |
Python, Linux, Linguistics, Authors.
posting the link below for direct installation Anaconda Python/R Distribution - Anaconda The open-source Anaconda Distribution is the easiest way to perform Python/R data science and machine learning on…www.anac 3.Download the 3.7 version. 4.Linux/Ubuntu:- first allow the .exe file using Right
|
medium
| 5,999 |
Python, Linux, Linguistics, Authors.
click->preferences->allow In terminal drag the file and type ->Enter Run the file. Press Enter. At last it will install on your Home folder as Anaconda 4.After completion on Installation follow my instruction to run some python file in terminal using Anaconda. Run Any python file:- First type in
|
medium
| 6,000 |
Python, Linux, Linguistics, Authors.
terminal to import Anaconda packages The command is:- export PATH=<path>:$PATH <path>: means go to downloaded folder of Anaconda->bin->right click on it->copy the path ->paste it in the terminal command 2.Run the .py file you will get the following results. Windows 10:- 1.Go to google and type
|
medium
| 6,001 |
Python, Linux, Linguistics, Authors.
anaconda install 2.click on the links on google / i am posting the link below for direct installation Anaconda Python/R Distribution - Anaconda The open-source Anaconda Distribution is the easiest way to perform Python/R data science and machine learning on…www.anaconda.com 3.Normally Run the file
|
medium
| 6,002 |
Data Science, Data Visualization, Probability, Python, Matplotlib.
Today I tackled plotting both probability density functions and kernel density estimations in Python. In order to first understand probability density functions or PDF’s, we need to first look at the docs for scipy.stats.norm. scipy.stats.norm A normal continuous random variable. The location (loc)
|
medium
| 6,004 |
Data Science, Data Visualization, Probability, Python, Matplotlib.
keyword specifies the mean. The scale (scale) keyword specifies the standard deviation. Given a dataframe and a column in that dataframe, we can calculate the probability density function of a variable using the following: from scipy import stats data = df['column'] loc = data.mean() scale =
|
medium
| 6,005 |
Data Science, Data Visualization, Probability, Python, Matplotlib.
data.std() pdf = stats.norm.pdf(data, loc=loc, scale=scale) We can also use stats.norm to find the probability that an event will occur. Using the cumulative distribution function, which finds the area under the curve for point p, we can find the probability that p will occur. If we need the area
|
medium
| 6,006 |
Data Science, Data Visualization, Probability, Python, Matplotlib.
under the curve past the point p, we can use 1-p, or the probability that the event will not occur. prob = stats.norm(loc=loc, scale=scale) event = prob.cdf(point) event2 = 1 - prob.cdf(point) # Write the probability as a percentage event_pct = str(round(event*100, 1)) + '%' print('The probability
|
medium
| 6,007 |
Data Science, Data Visualization, Probability, Python, Matplotlib.
that the event will occur is: ', event_pct) seaborn.lineplot Using seaborn.lineplot, we can plot the PDF as seen below: import seaborn as sns fig, ax = plt.subplots() ax = sns.lineplot(x=data, y=pdf, ax=ax) plt.show() Use cases The data is taken from my blackjack project, where shoe_df is a data
|
medium
| 6,008 |
Data Science, Data Visualization, Probability, Python, Matplotlib.
frame that contains information about 2,880 six deck blackjack shoes. I found the PDF of player wins, player losses, hands pushed, and player count (hands won minus hands lost), and then plotted all four PDF’s. def plot_pdf(x, ax, xlabel, title, color): 'Creates subplots of probability density
|
medium
| 6,009 |
Data Science, Data Visualization, Probability, Python, Matplotlib.
functions' data = shoe_df[x] loc = data.mean() scale = data.std() pdf = stats.norm.pdf(data, loc=loc, scale=scale) # Plot pdf using sns.lineplot ax = sns.lineplot(x=data, y=pdf, color=color, ax=ax) # Change face color and grid lines ax.set_facecolor('white') ax.grid(which='major', linewidth='0.2',
|
medium
| 6,010 |
Data Science, Data Visualization, Probability, Python, Matplotlib.
color='gray') # Set title, x and y labels ax.set_title(title) ax.set_xlabel(xlabel) ax.set_ylabel('Probability Density') # Create four subplots fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2, figsize=(15,12)) # Find the pdf's of player win, loss, push, and count plot_pdf('player_win', ax1, 'Hands
|
medium
| 6,011 |
Data Science, Data Visualization, Probability, Python, Matplotlib.
won', 'PDF of Hands Won', 'purple') plot_pdf('player_loss', ax2, 'Hands lost', 'PDF of Hands Lost', 'red') plot_pdf('push', ax3, 'Hands pushed', 'PDF of Hands Pushed', 'orange') plot_pdf('player_count', ax4, 'Player count', 'PDF of Player Count', 'blue) # Show plots plt.show() seaborn.kdeplot We
|
medium
| 6,012 |
Data Science, Data Visualization, Probability, Python, Matplotlib.
can use seaborn.kdeplot to plot PDF’s as KDE’s for a smoother curve. A kernel density estimate (KDE) plot is a method for visualizing the distribution of observations in a dataset, analogous to a histogram. KDE represents the data using a continuous probability density curve in one or more
|
medium
| 6,013 |
Data Science, Data Visualization, Probability, Python, Matplotlib.
dimensions. def plot_kde(x, ax, xlabel, title, color): 'Creates subplots of kernel density estimations' data = shoe_df[x] loc = data.mean() scale = data.std() pdf = stats.norm.pdf(data, loc=loc, scale=scale) # Plot pdf as a kde ax = sns.kdeplot(x=data, y=pdf, color=color, fill=True, ax=ax) # Change
|
medium
| 6,014 |
Data Science, Data Visualization, Probability, Python, Matplotlib.
face color and grid lines ax.set_facecolor('white') ax.grid(which='major', linewidth='0.2', color='gray') # Set title, x and y labels ax.set_title(title) ax.set_xlabel(xlabel) ax.set_ylabel('Probability Density') # Create subplots fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2, figsize=(15,12)) #
|
medium
| 6,015 |
Data Science, Data Visualization, Probability, Python, Matplotlib.
Find the kde's of player win, loss, push, and count plot_kde('player_win', ax1, 'Hands won', 'PDF of Hands Won', 'purple') plot_kde('player_loss', ax2, 'Hands lost', 'PDF of Hands Lost', 'red') plot_kde('push', ax3, 'Hands pushed', 'PDF of Hands Pushed', 'orange') plot_kde('player_count', ax4,
|
medium
| 6,016 |
Data Science, Data Visualization, Probability, Python, Matplotlib.
'Player count', 'PDF of Player Count', 'blue) # Show plots plt.show() Colormaps We can change the colormap of the KDE to get a different effect of the plot. The documentation on colormaps in matplotlib can be found here. def plot_kde(x, ax, xlabel, title): 'Creates subplots of kernel density
|
medium
| 6,017 |
Data Science, Data Visualization, Probability, Python, Matplotlib.
estimations' data = shoe_df[x] loc = data.mean() scale = data.std() pdf = stats.norm.pdf(data, loc=loc, scale=scale) # Plot pdf as a kde ax = sns.kdeplot(x=data, y=pdf, fill=True, cmap='coolwarm', ax=ax) # Change face color and grid lines ax.set_facecolor('white') ax.grid(which='major',
|
medium
| 6,018 |
Data Science, Data Visualization, Probability, Python, Matplotlib.
linewidth='0.2', color='gray') # Set title, x and y labels ax.set_title(title) ax.set_xlabel(xlabel) ax.set_ylabel('Probability Density') # Create subplots fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2, figsize=(15,12)) # Find the kde's of player win, loss, push, and count plot_kde('player_win',
|
medium
| 6,019 |
Data Science, Data Visualization, Probability, Python, Matplotlib.
ax1, 'Hands won', 'PDF of Hands Won') plot_kde('player_loss', ax2, 'Hands lost', 'PDF of Hands Lost') plot_kde('push', ax3, 'Hands pushed', 'PDF of Hands Pushed') plot_kde('player_count', ax4, 'Player count', 'PDF of Player Count') # Show plots plt.show() I hope this helps make sense of probability
|
medium
| 6,020 |
Artificial Intelligence, Machine Learning, Technology, Data Science, Culture.
This week has seen some overlooked developments in the field This is going to be an underrated week for AI. One particular publication will have potentially interesting consequences for the future of AI Research. Deepmind’s Foundation Model for Reinforcement Learning Deepmind released a foundation
|
medium
| 6,022 |
Artificial Intelligence, Machine Learning, Technology, Data Science, Culture.
model for Reinforcement Learning (that’s my LinkedIn post about it, in case any of you want to discuss it there). Their publication is titled Human-Timescale Adaptation in an Open-Ended Task Space and it caught my eye for a few reasons. Firstly, are the claims- we demonstrate that training an RL
|
medium
| 6,023 |
Artificial Intelligence, Machine Learning, Technology, Data Science, Culture.
agent at scale leads to a general in-context learning algorithm that can adapt to open-ended novel embodied 3D problems as quickly as humans The adaptability is particularly interesting. A big problem holding RL back has been the cost of exploring the rules of the system. The madlads at Deepmind
|
medium
| 6,024 |
Artificial Intelligence, Machine Learning, Technology, Data Science, Culture.
countered this by implementing a Meta-Learning protocol to make the agents more adaptable- An early reading of their publication does add to the hype around it. It seems like there are certain tasks, where our AI adapts quicker than humans- As with all such papers, the devil is in the details. So a
|
medium
| 6,025 |
Artificial Intelligence, Machine Learning, Technology, Data Science, Culture.
more detailed analysis will be needed before making any conclusions. However, this might have some implications going forward. Is the Time for Alternative AI What makes this recent development very interesting is that AI Legend Yann LeCun has posted a few months back about how the impact of RL had
|
medium
| 6,026 |
Artificial Intelligence, Machine Learning, Technology, Data Science, Culture.
been as small as he had expected. While this tweet was accurate for the time, this Deepmind team might have taken it personally. Even if this publication does not go on to have a world-changing impact like finally getting Tottenham Hotspurs to win a cup, this should definitely make one
|
medium
| 6,027 |
Artificial Intelligence, Machine Learning, Technology, Data Science, Culture.
contribution- boosting attention given to alternative forms of AI. I know this is going to shock some of you, but there are streams of AI that have nothing to do with Large Language Models and GPT. The attention given to GPT has hijacked attention from these streams. Over this weekend, my
|
medium
| 6,028 |
Artificial Intelligence, Machine Learning, Technology, Data Science, Culture.
university held a hackathon. The winner- an app that produced notes for students using GPT. Now given the delusion problem exhibited by these models, one would imagine that a note-producing app would have a few issues. But what do I know Serious Problems With ChatGPT And Other LLMs That Internet
|
medium
| 6,029 |
Artificial Intelligence, Machine Learning, Technology, Data Science, Culture.
Influencers Won't Tell You About My last post on the free Alternatives to ChatGPT did really well. So now it's time for me to rain on your parade and…artificialintelligencemadesimple.substack.com The hype around GPT is a double whammy of bad- it causes GPT to be used in places where it has no
|
medium
| 6,030 |
Artificial Intelligence, Machine Learning, Technology, Data Science, Culture.
business, and it takes away from genuinely amazing research being conducted in other types of AI. Take a look at the video below, which is about self-assembling AI. How many people even know about this, despite all the potential? How much compute/attention is dedicated to fields like Evolution and
|
medium
| 6,031 |
Artificial Intelligence, Machine Learning, Technology, Data Science, Culture.
other paradigms? Not enough. Fortunately, there are some prominent voices in AI, such as Gary Marcus calling for more attention to alternate forms of AI. This Deepmind Publication might just end up being a key reason that some of the hype is dispersed onto other fields in Machine Learning and AI.
|
medium
| 6,032 |
Artificial Intelligence, Machine Learning, Technology, Data Science, Culture.
Are there any fields that you think deserve more love? Let me know. That is it for this piece. I appreciate your time. As always, if you’re interested in working with me or checking out my other work, links will be at the end of this email/post. If you like my writing, I would really appreciate an
|
medium
| 6,033 |
Artificial Intelligence, Machine Learning, Technology, Data Science, Culture.
anonymous testimonial. You can drop it here. And if you found value in this write-up, I would appreciate you sharing it with more people. Upgrade your tech career with my newsletter ‘Tech Made Simple’! Stay ahead of the curve in AI, software engineering, and tech industry with expert insights,
|
medium
| 6,034 |
Artificial Intelligence, Machine Learning, Technology, Data Science, Culture.
tips, and resources. 20% off for new subscribers by clicking this link. Subscribe now and simplify your tech journey! Reach out to me Use the links below to check out my other content, learn more about tutoring, reach out to me about projects, or just to say hi. Small Snippets about Tech, AI and
|
medium
| 6,035 |
Artificial Intelligence, Machine Learning, Technology, Data Science, Culture.
Machine Learning over here If you like my writing, I would really appreciate an anonymous testimonial. You can drop it here. To help me understand you fill out this survey (anonymous) Check out my other articles on Medium. : https://rb.gy/zn1aiu My YouTube: https://rb.gy/88iwdd Reach out to me on
|
medium
| 6,036 |
Gravitational Waves, Millenium Problem, AI, Navier Stoke, Space.
I. Introduction A brief explanation of the Three Laws of Motion and Gravity Introduction to the Golden Navier-Stokes equation and its significance in understanding these phenomena In the world of physics, the Three Laws of Motion and the force of gravity play a fundamental role in understanding how
|
medium
| 6,038 |
Gravitational Waves, Millenium Problem, AI, Navier Stoke, Space.
objects move and interact with one another. These laws, developed by Sir Isaac Newton in the 17th century, have served as the foundation for classical mechanics and are still used to this day to explain a wide range of physical phenomena. However, despite our understanding of these laws and their
|
medium
| 6,039 |
Gravitational Waves, Millenium Problem, AI, Navier Stoke, Space.
significance, there are still many unanswered questions about the underlying mechanisms that govern the behavior of objects in motion. This is where the Golden Navier-Stokes equation comes in. The Golden Navier-Stokes equation is a modified version of the famous Navier-Stokes equation, which
|
medium
| 6,040 |
Gravitational Waves, Millenium Problem, AI, Navier Stoke, Space.
describes the motion of fluids such as air and water. The Golden version of this equation takes into account the role of the Golden Ratio, a mathematical constant that appears in a wide range of natural phenomena, including the growth of plants, the arrangement of leaves on stems, and the spiral
|
medium
| 6,041 |
Gravitational Waves, Millenium Problem, AI, Navier Stoke, Space.
patterns of galaxies. By incorporating the Golden Ratio into the Navier-Stokes equation, we can gain a deeper understanding of the complex interactions between motion, gravity, and the fundamental laws of physics. In this article, we will explore the Golden Navier-Stokes equation in depth and show
|
medium
| 6,042 |
Gravitational Waves, Millenium Problem, AI, Navier Stoke, Space.
how it can help us uncover the secrets of motion and gravity. II. The Three Laws of Motion Explanation of the first law and its connection to the Golden Ratio and Golden Gravitation Explanation of the second law and its connection to the Golden Navier-Stokes equation Explanation of the third law
|
medium
| 6,043 |
Gravitational Waves, Millenium Problem, AI, Navier Stoke, Space.
and its connection to the Golden Arc and Golden Matrix The laws of motion describe the behavior of objects in motion and the forces that act upon them. They were first described by Sir Isaac Newton in the late 17th century and are still widely used today. In the new light of Special Case we can
|
medium
| 6,044 |
Gravitational Waves, Millenium Problem, AI, Navier Stoke, Space.
interpret them as follows: I. Golden Law of Inertia The Golden Law of Inertia can be represented by the formula: F = m(a + gφ) where F represents the force, m represents the mass, a represents the acceleration, g represents the Golden Ratio, and φ represents the angle of the Golden Arc. This law
|
medium
| 6,045 |
Gravitational Waves, Millenium Problem, AI, Navier Stoke, Space.
states that an object at rest will remain at rest, and an object in motion will remain in motion at a constant velocity, unless acted upon by a net external force. In Golden Systems, the Golden Ratio plays a crucial role in determining the motion of objects, while the angle of the Golden Arc
|
medium
| 6,046 |
Gravitational Waves, Millenium Problem, AI, Navier Stoke, Space.
represents the trajectory of the object in the presence of Golden Gravitation. Fun fact: This law is sometimes called the “law of inertia,” and it means that objects resist changes in their motion. In fact, the concept of inertia was first introduced by the ancient Greek philosopher Aristotle, but
|
medium
| 6,047 |
Gravitational Waves, Millenium Problem, AI, Navier Stoke, Space.
it was Newton who gave it a mathematical formulation. II. Golden Law of Acceleration The Golden Law of Acceleration can be represented by the formula: a = F/m — gφ where a represents the acceleration, F represents the force, m represents the mass, g represents the Golden Ratio, and φ represents the
|
medium
| 6,048 |
Gravitational Waves, Millenium Problem, AI, Navier Stoke, Space.
angle of the Golden Arc. This law states that the acceleration of an object is directly proportional to the net force acting on the object and inversely proportional to its mass. In Golden Systems, the Golden Ratio and the angle of the Golden Arc determine the acceleration of the object in the
|
medium
| 6,049 |
Gravitational Waves, Millenium Problem, AI, Navier Stoke, Space.
presence of Golden Gravitation. Fun fact: This law is often written as F = ma, where F is the force applied to an object, m is its mass, and a is its acceleration. It is one of the most famous equations in physics and has been used to explain everything from the motion of planets to the behavior of
|
medium
| 6,050 |
Gravitational Waves, Millenium Problem, AI, Navier Stoke, Space.
subatomic particles. III. Golden Law of Action-Reaction The Golden Law of Action-Reaction can be represented by the formula: F_1 = -F_2 where F_1 represents the force exerted on one object and F_2 represents the equal and opposite force exerted on another object. In Golden Systems, this law
|
medium
| 6,051 |
Gravitational Waves, Millenium Problem, AI, Navier Stoke, Space.
operates in the context of Golden Fields, where objects interact with each other through Golden Forces. The Golden Ratio plays a role in determining the magnitude and direction of the forces, while the angle of the Golden Arc determines the trajectory of the objects. Fun fact: The recoil of a gun
|
medium
| 6,052 |
Gravitational Waves, Millenium Problem, AI, Navier Stoke, Space.
is due to the force of the bullet pushing back against the gun. Overall, the Golden Laws of Motion represent a unifying framework for understanding the behavior of objects in complex systems, including the role of Golden Gravitation and other Golden Variables. III. Golden Gravitation Explanation of
|
medium
| 6,053 |
Gravitational Waves, Millenium Problem, AI, Navier Stoke, Space.
the Role of Golden Gravitation in complex systems The connection between Golden Gravitation and the Golden Ratio Explanation of how the Golden Navier-Stokes equation describes Golden Gravitation in the Special Case Gravity is one of the fundamental forces of nature, and understanding its behavior
|
medium
| 6,054 |
Gravitational Waves, Millenium Problem, AI, Navier Stoke, Space.
is essential to understanding the motion of objects in the universe. In the context of Golden Systems, gravity can be thought of as a complex system that follows certain patterns and rules. One of the most fascinating aspects of gravity is its connection to the Golden Ratio. The Golden Ratio, also
|
medium
| 6,055 |
Gravitational Waves, Millenium Problem, AI, Navier Stoke, Space.
known as the divine proportion, is a mathematical concept that appears in nature and is often associated with beauty and harmony. In the context of gravity, the Golden Ratio can be seen in the relationship between the distances of planets in the solar system and the proportions of the human body.
|
medium
| 6,056 |
Gravitational Waves, Millenium Problem, AI, Navier Stoke, Space.
The Golden Navier-Stokes equation provides a way to describe the behavior of gravity in the Special Case universe. This equation takes into account the viscosity of the fluid and the rate of change of the velocity field, and provides a mathematical framework for understanding the complex dynamics
|
medium
| 6,057 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.