Composer is a dependency manager for PHP applications. It allows you to install, update, and manage third-party libraries for your Laravel project. Laravel uses Composer to manage its own dependencies, as well as the dependencies of any packages you install.

To install Composer on Windows, you can follow these steps:

1. Go to https://getcomposer.org/download/ in your web browser.

2. Download the Composer-Setup.exe file by clicking on the “Windows Installer” option.

3. Once the file is downloaded, double-click on it to run the installer.

4. The installer will guide you through the setup process. You can leave the default settings as they are or customize them as per your preference.

5. When you reach the “Installation Options” screen, make sure the “Add Composer to PATH” option is checked. This will allow you to use Composer from any directory in the Command Prompt.

6. Continue with the installation by following the remaining on-screen instructions.

7. After the installation finishes, open a new Command Prompt window (or restart it if it was already open).

8. To verify if Composer is installed properly, type “composer” in the Command Prompt and press Enter. It should display the Composer version and a list of available commands.

That’s it! Composer is now installed on your Windows system, and you can start using it for managing your PHP dependencies.

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Today, the role of artificial intelligence in an enterprise has become so important that it has touched every facet of business. Its role will become more critical in the years to come.

For the purposes of this article, let us define A.I. as follows. Human intelligence is learnt from experience. Machines so far have been primarily used to follow instructions, i.e. programmed, hence machines have provided automation based on rules.

A.I. is not programmed to follow rules, it is like human intelligence, learns from “experience”, i.e. data.

A.I. application in businesses today can be divided into 5 key areas:

1. A.I. in data cleansing and streamlining.

2. A.I. in BI—i.e. AI to replace business analysts in preliminary analysis on dashboards.

3. A.I. in cognitive intelligence such as voice recognition, video analytics, face recognition.

4. A.I. in natural interaction – chat bots, NLP, Natural-Language Generation (NLG).

5. A.I. in expert systems—learning from myriad data sets and crystallizing an insight or action. This can be applied in classification of future unknowns e.g. fraud prevention, preventive maintenance. This can be applied also in forecasting quantities e.g. demand forecasting, supply shortage prediction. And can be applied in real time dynamic operations e.g. self-driving cars, dynamic digital marketing.

A.I. in data cleansing

There are A.I. applications today that can weed out errors in master data. In fact, a business was able to reduce their data errors in incoming data by 94% through A.I. application in data error correction. Humans used to correct such errors before and the correction was based on knowledge about the product, data on similar products and so on. If a product is a liquid and the units of measure are missing, the human planner used to correct that gap looking at the product being replaced by this new product or other liquid products in the same price range and so on, or the image of the product itself. AI now does the same and has been seen to be able to correct 94% of data errors without human help.

A.I. in BI

We know when reports and dashboards are delivered, junior analysts typically do a pre-analysis to ascertain exceptions, and explanations for those exceptions. These analysts circle these exceptions and comment on the components contributing to the exception. They also may mark something as high priority if an urgent action is needed. This saves the decision maker’s time and presents them with distilled information. Today, A.I. is able to do this – augmenting junior analysts using anomaly detection and auto-drill down, pattern recognition and clustering.

A.I. in cognitive intelligence

The usual examples people think of when talking of AI applications is in cognitive intelligence. As an example, in this pandemic, cognitive systems are tracking masks wearing compliance in closed spaces, distancing norms compliance in factories and warehouses. Cognitive intelligence is also used in less known forms, there is a recent MIT report on an AI model that can detect Covid-19 infection from your cough, being incorporated into an FDA approved cell phone app.

A.I. in natural interaction

Chat bots are here to stay. If we haven’t interacted with one yet, we are probably not clicking on that chat button on most online shopping sites. It is interesting that A.I. learning is not being achieved in most chat bots from chat histories of those businesses alone, but using innovative data sources such as Q&A dialogues in published interviews, consumer panel discussions, even published plays, and so on.

The way personalisation is achieved is even more interesting. If A.I. was simply replacing a human agent the chat would begin with “how may I help you?”. However, A.I. is much more than that. There are call intent prediction models which learn from the behavior of customers who had a similar interaction, purchase history and profile. Gleaning from data, what did such cohorts mostly call about when they did call? So, the conversation starts with “Are you calling about your oximeter ordered yesterday?”

A.I. in expert systems

This is the area impacted most post pandemic. For example, demand forecasting of CPG products using traditional techniques became impossible. However, applying A.I. made a difference.

For example, loyalty card identifiers in transaction data helped A.I. learn that there are some products which saw an upswing in sales that would continue (because the same shoppers were re-purchasing high quantities). Products like soap, sanitizers, home cleaners, etc. were identified for continued upswing from such analysis. Other items that did see an upswing but were unlikely to sustain (because the same shopper was not repurchasing high quantities) were items like paper towels, pet food, baby food, etc. This kind of ability in the first two months of dynamic demand helped many firms to plan better for the supply and allocation across locations.

With digital interactions rising, application of A.I. for a cookie less world has led to Federated learning of cohorts (FLOCs). FLOCs use models learning from customer cohort behavior through distributed data, without transferring data to a central server, thus protecting privacy.

What has been true of times of upheaval, applies to this one as well. It is a time of opportunity for those who leverage available tools including A.I. to transform their business to gain competitive advantage. Others who delay, cannot afford to miss this revolution. They will be late and may struggle, if they sustain at all.

The post The critical role of A.I. in an enterprise today appeared first on Artificial Intelligence.

A German-American team of scientists have deciphered some of the more elusive instructions encoded in DNA with the help of artificial intelligence (AI). Their neural network trained on high-resolution maps of protein-DNA interactions uncovers subtle DNA sequence patterns throughout the genome, thus providing a deeper understanding of how these sequences are organised to regulate genes.

Artificial intelligence algorithms are extremely powerful at fitting massive and complex datasets. But their interpretation, rationalising how the machine performs specific predictions when presented a given input, is notoriously hard. This black box behaviour hampers wide acceptation of AI in medical diagnostics, where justifications matter, and restrain their utility in natural sciences where understanding mechanisms is the goal.

‘Neural networks are black boxes, but they can be interrogated digitally. So, with a large number of virtual experiments, we figured out the rules the neural net learned’ says first author Dr Žiga Avsec, member of the group of Julien Gagneur, professor of computational molecular medicine at the Technical University of Munich. Together with Anshul Kundaje, professor at Stanford University, he created the first version of the model when he visited Stanford as a guest scientist. An interdisciplinary team of biologists and computational researchers continued this research and have now shown that neural networks can be used to decipher complex instructions encoded in DNA.

Researchers working on this project from the Technical University of Munich, the Stowers Institute for Medical Research and Stanford University continued are employing neural networks, such as those used for facial recognition, together with newly developed model interpretation techniques that can be used to decipher complex instructions encoded in DNA.

One of the big unsolved problems in biology is the genome’s second code, its regulatory code. The DNA bases encode not only the instructions for how to build proteins, but also when and where to make these proteins in an organism.

The regulatory code is read by proteins called transcription factors that bind to short stretches of DNA called motifs. However, how particular combinations and arrangements of motifs specify regulatory activity is an extremely complex problem that has been hard to pin down.

DNA binding experiments and computational modelling going hand in hand

The key was to perform transcription factor-DNA binding experiments and computational modelling at the highest possible resolution, down to the level of individual DNA bases. The increased resolution allowed the team not only to train highly accurate neural network models but also to extract the key elements and patterns from the models, including transcription factor binding motifs and the combinatorial rules by which they function together as code.

Applied to master regulators of stem cell differentiation and confirmed experimentally by CRISPR, the approach revealed complex rules involving a precise positioning along the DNA double helix and specific ordering of events.

‘This was extremely satisfying,’ commented project leader Julia Zeitlinger, investigator at the Stowers Institute and professor at the University of Kansas Medical Center, ‘as the results fit beautifully with existing experimental results, and also revealed novel insights that surprised us.’

A pattern becomes visible: how Nanog binds to DNA

For example, the researchers found that a well-studied transcription factor called Nanog binds cooperatively to DNA when multiples of its motif are present in a periodic fashion such that they appear on the same side of the spiralling DNA helix.

‘There has been a long trail of experimental evidence that such motif periodicity sometimes exists in the regulatory code,’ Zeitlinger stated. However, the exact circumstances were elusive, and Nanog had not been a suspect. Discovering that Nanog has such a pattern, and seeing additional details of its interactions, was surprising because we did not specifically search for this pattern.”

‘This is the key advantage of using neural networks for this task. A classic computational model is built on hand-crafted, rigid rules to ensure that it can be interpreted,’ says Avsec. ‘However, biology is extremely rich and complicated. By abandoning the need to interpret individual parameters, we can train much more flexible and nuanced models that capture any biological phenomena, including those yet unknown.’

A powerful bottom-up approach

This neural net model – named BPNet for Base Pair Network – is a powerful bottom-up approach similar to facial recognition in images, where a neural network first detects edges in the pixels, then learns how edges form facial elements like the eye, nose or mouth, and finally how facial elements together form a face.

Instead of learning from pixels, BPNet learns from the raw DNA sequence and learns to detect sequence motifs and eventually the higher-order rules by which the elements predict the base-resolution binding data.

Both the Zeitlinger Lab and the Kundaje Lab are already using BPNet to reliably identify binding motifs for other cell types, relate motifs to biophysical parameters and learn other structural features in the genome such as those associated with DNA packaging. To enable other scientists to use BPNet and adapt it for their own needs, the researchers have made the entire software framework available with documentation and tutorials.

This work was supported by in part by the Stowers Institute for Medical Research and the National Human Genome Research Institute and National Institute of General Medical Sciences of the National Institutes of Health (NIH). Additional support included the German Federal Ministry of Education and Research and a Stanford BioX Fellowship and Howard Hughes Medical Institute International Student Research Fellowship.

Gene sequencing was performed at the Stowers Institute for Medical Research and the University of Kansas Medical Center Genomics Core supported by the NIH awards from the National Institute of Child Health and Human Development and the National Institute of General Medical Sciences.

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