ZIVA VFX is a Maya plug-in with an offline FEM soft tissue simulator that lets you create life-like characters driven by anatomy and physics

Ziva Dynamics

Marc Andreessen famously claimed, as far back as 2011, that “software is eating the world,” and that it would radically disrupt businesses across industries. This prediction has come true as software has infiltrated every facet of personal life, work life, and industry. A decade later, I claim that we are seeing signs that “game engines are eating software.” But first, what is a game engine? A game engine is a set of programmable software and tools that allow artists to create interactive virtual environments for games, not just games. This software will act as a physics simulator - accurately modeling kinematics, sound, and lighting. The obvious use case for this technology is to create 2D or 3D interactive environments for games, hence the name. In the early days of gaming, game developers would create their own proprietary game engines which took considerable development time and resources to build and maintain. Today, the market is dominated by Unity and Epic Games (the maker of ). Graphics chip maker NVIDIA is slowly inching towards building a game engine of its own but may focus more on industrial simulation than the incumbents. Unreal Engine ​ So why would a tool designed for game development capture the software market as a whole, and why now? A confluence of factors are arising that may signal exponential growth: Real-time photorealism capabilities that now compete with special effects shops Growing demand for simulation (especially for training AI) Growing demand for responsive user interfaces The vast availability of inexpensive GPUs in SoCs originally developed for smartphones Game engines have reached a level of photorealism in real-time comparable with special effects for big-budget movies that had taken hours of render time per frame. Real-time ray tracing (such as Nvidia’s RTX) generates a massive step change in lighting realism, Hair and water simulation packages from and soft-tissue simulation tech from enable extremely realistic environments and character animation. , NVIDIA Gameworks Ziva Dynamics Real-time generation of photorealistic special effects and virtual environments may radically disrupt the cost structure of traditional entertainment that is currently reliant on special effects shops. In fact, some studios are already using game engines to dramatically reduce the cost of filming. For example, Disney uses Unreal Engine to , and Unity has been used as a rendering engine for . create virtual backgrounds in The Mandalorian many animated films More importantly, this technology opens up entirely new entertainment mediums such as “choose your adventure” TV shows or movies with customizable levels of violence and gore. Even more impactful: the use of game engines in entertainment enables the mass availability of (video that has 3D data and allows the user to move the camera and see a scene from different perspectives). VR is a particularly immersive way to enjoy volumetric video. volumetric video Game engines are effective, highly accurate and performant physics engines. As a result, they have great potential for and generating training data (although there is always the risk of ). digital twins overfitting to generate difficult or dangerous scenarios in which to train its Autopilot AI. Nvidia created the to train massive numbers of picking robots in parallel. However, there may be an even greater market opportunity in industrial and manufacturing, where sensor data can be compared against digital twin simulations in real-time to diagnose and preemptively maintain key machinery. Tesla uses game engines virtual environment simulator Isaac These applications, although important and large opportunities in themselves, represent only a tiny fraction of the potential of game engines. I believe that game engines will eventually eat user interfaces. Users demand responsive applications and have a growing expectation that objects and interfaces should be interactive. For example, in maps, a user expects to be able to pan and zoom fluidly as well as reorient the camera and have 3D buildings react appropriately to virtual lights. In a car interface, users expect responsiveness and the ability to pan around their car’s avatar to open trunks and lock doors (Tesla has an exceptional UI in this regard). Even simple devices like dishwashers, refrigerators, and other household appliances will need simple, clear, but dynamic and fluid user interfaces. Game engines provide an easy-to-use editor for UI that is responsive and dynamic. This provides non-experts an easy and fast way to build a UI that customers love, rather than the more time-intensive process of building a UI from scratch. WYSIWYG ​ One argument against the use of game engines for UI is the increased hardware requirement. After all, why should a car or a refrigerator have a GPU? However, the exponential decline in prices for ARM , plus the proliferation of Android into more home devices, means that these everyday objects not only have a GPU but are running on an OS that game engines have relentlessly optimized for. In such a case, using a game engine for a UI is effectively wasting the perfectly good silicon already in the device. It is likely that these SoCs will have vastly underpowered GPUs compared to their smartphone counterparts, but they will still be more than capable of running a simple UI in a highly optimized game engine. SoCs not ​ Unity and Epic Games are aggressively building out lessons and course plans to use their software, hoping that they become the de-facto programming choice for new developers. Unity in particular has targeted children with its . The nature of game engines lends itself well to learning programming, as it enables real-time visual “debugging” of the code as users can see in real-time how the code works. Lego-themed unity programming courses ​ The confluence of these factors presents a bullish future for game engines. Will incumbent game engines iterate and innovate quickly enough to become the de facto simulation and user interface layer of the future, or will new startups built from the ground up for these roles seize this opportunity? Is it possible that we will live in a future where all front-ends are built on Unity or Unreal and all back ends are built on AWS, Azure, or Google Cloud? Or, if you want to get really philosophical, ? are we living in a game engine A Wildcard: The Metaverse The Metaverse means many different things to many different people and has become somewhat meaningless, just like once technical, now marketing terms such as AR, XR, and MR before it. Some people believe that the Metaverse needs to be a decentralized and permissionless network, others believe that it needs to be completely run in virtual reality, and there are many shades in between. I have avoided discussing it for this reason, and because it remains unclear if end users will actually prefer the Metaverse to the status quo, despite the advantages of working, shopping, and socializing in a virtual environment. Regardless of your definition, most interpretations of the Metaverse believe that it requires some sort of 2D or 3D interactive environment, and this provides a massive opportunity for game engines to eat apps and the entire web as we know it today. Also published here.

As Software Eats the World, Game Engines Eat Software 

254 Stories To Learn About Healthcare

Proteins It is safe to say are and the which defines . In the last 70 years tremendous progress has been made in their isolation, production, characterization, and finally engineering. Although great advancements in laboratory and industrial-scale protein production have been made, protein engineering and all the associated steps remain , and truly . proteins building blocks machinery living matter laborious expensive complicated If you prefer to skip further reading, and start hacking straight-away simply visit our 👉🏼 Github repository at: https://github.com/PeptoneInc/dspp-keras Proteins are polymers Proteins are complex biomolecules made of , , which are connected sequentially into ; commonly known as . 20 building blocks amino acids long non-branching chains polypeptide chains Unique of polypeptide chains yields structures, which define protein function and interactions with other biomolecules. spatial arrangement 3D molecular Although the very basic that govern protein 3D structure formation are and , the exact nature of polypeptide remains and has been studied extensively for the past 50 years. forces known understood folding elusive Protein engineering is complex We want to engineer proteins to enhance their properties. Typically, under different , or . Frequently, researchers are aiming at improving of protein enzymes, or adding completely new types of to known proteins. stability temperatures pH salinity catalytic performance chemical activities The most common and established way to a protein is to create its with substituted amino acids, also known as . Subsequently, newly produced mutants are characterized using various experimental techniques to measure the degree of enhancement; e.g. scanning calorimetry, isoelectric point determination, simple solubility studies or advanced enzymatic activity assays. However, since there are protein amino acids, a would yield engineer variants mutants 20 standard complete mutagenesis of 100-residue long polypeptide 20¹⁰⁰ mutant combinations, should you decide to explore all possible combinations of typical protein amino acids. Exploring all possible mutants of a 100 amino acid protein (polypeptide) requires more time than the age of known universe, given you could express, purify and characterize each variant in 1 second time. 100,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000 Quite likely, only a marginal fraction of the mutants would have desired properties, as usually ( ). the more you change the protein the further you step away from its original function this is absolutely not a rule, as it is protein specific. However, a logical consequence of replacing a major part of protein with completely new amino acid sequence will likely be new fold, hence new functionality. Moreover, I have intentionally left out a fundamentally important fact — mutations may significantly affect protein dynamics, and thus its function Protein biotechnology is to a large extent hampered by scale and of mutational analysis. complexity How can Machine Learning accelerate progress in protein science? The most and (Bayesian) variants of heavily on the . This argument is especially for and techniques in , where the levels of model are perplexing or simply . advanced probabilistic Machine Learning depend size and quality of input data important inference prediction life sciences complexity unknown Protein structure, function and dynamics predictions through Machine Learning methodology are not an exception. However, even with the (compared to a number of possible combinations of all protein amino acids in lengthy polypeptide chains) protein Machine Learning can help to , non-linear between and their and . These relationships are either very to model or simply . The predictions are coarse. relatively sparse databases, unravel complex relationships protein sequences structural variability dynamics difficult not fully understood The biggest of Machine Learning methods in of biophysical properties of proteins is their ability to “ ” loosely protein to measurable . Thus predictions using complex numerical models that underlie Machine Learning methodology, can be further tweaked and refined by providing independent experimental proxies of protein structure and dynamics. value prediction equate related features experimental data A simplified connectivity diagram of methodology developed at . Hybrid Machine Learning Peptone — The Protein Intelligence Company Proteins are dynamic and exhibit variable degrees of disorder Just like every other molecule present in our natural environment, undergo at time scales ranging from to . polypeptide chains molecular motions nanoseconds minutes It is accepted that complete understanding of protein functions and activity requires knowledge of structures and dynamics. is a very peculiar of many known and characterised proteins. It has been attributed to , and it has an immediate consequence for protein , to enzymatic digestion inside living cells, protein-protein and in turn a decisive role in many . Structural disorder property specific patterns in protein sequence stability susceptibility interactions debilitating human pathologies From an industrial biotechnology point of view, the ability to accurately of amino acid mutations in engineered proteins can . An accurate disorder prediction for an arbitrary protein mutant can immediately report on problematic combinations of amino acid sequences, thus excluding the residues from further mutational analysis and vastly reducing the mutation search space. discern disordering effects save vast amounts of time and resources Singular protein structure model is not enough Under of living organisms ( ) in an aqueous environment, the can be thought of as an , which at any given moment in time have slightly different conformations, as a consequence of protein and “ . conditions aka native conditions state of an arbitrary polypeptide ensemble of structures dynamics intrinsic flexibility” or simply disorder MOAG-4 ensemble model has been kindly provided by Dr. Frans A.A. Mulder (Aarhus University, DK) and Dr. Predrag Kukic (University of Cambridge, UK). Please read “MOAG-4 Promotes the Aggregation of α-Synuclein by Competing with Self-Protective Electrostatic Interactions” to learn more about this protein and its medical relevance. The image above demonstrates the superposition of models belonging to a structural ensemble of MOAG-4 protein, . You can infer from this model that MOAG-4 has a relatively stable (well-defined) alpha-helical structure colored in grey, and a highly disordered tail, depicted by floating polypeptide chains of individual ensemble members. which in turn controls aggregation of proteins implicated in Parkinson’s disease Structural disorder is a medically relevant example of a protein that exhibits a high degree of intrinsic structural disorder. MOAG-4 (dSPP27058_0 in our database) The Alpha-synuclein ensemble has been adopted from “Structural Ensembles of Membrane-bound α-Synuclein Reveal the Molecular Determinants of Synaptic Vesicle Affinity” . Alpha-synuclein, pictured above, is a seminal example of a completely disordered protein. Although the ensemble of Alpha-synuclein is heterogenous, this protein plays an important role in neurotransmitter mediation in human brain, and . has been implicated as the key player in Parkinson’s disease development Putting protein order and disorder together Among the multitude of advanced experimental protein techniques, offers exquisite and at a . We have used NMR resonance assignment data from 7200+ proteins stored in public repositories and computed sequence-specific . NMR spectroscopy sensitivity to structural detail dynamics single residue level propensity scores The ensemble behavior of partially disordered MOAG-4 can be characterized and “ (with few critical assumptions ) to a structural propensity vector. compressed” discussed in our paper Importantly, our method excels at capturing , as seen in the example of intrinsically disordered Alpha-synuclein. residual intrinsic disorder Getting started with the data for Machine Learning In order to facilitate open source development of Machine Learning methodologies for disorder and order predictions we have prepared our dSPP data in a Keras, Tensorflow and Edward friendly forms . Simply check our . Github repository To install the integration, just type the following in your Terminal, dspp-keras pip install dspp-keras How to use to train models? dspp-keras To load the dataset in your Python models, use the module. dsppkeras.datasets from dsppkeras.datasets import dsppX, Y = dspp.load_data() How to encode protein sequence for Machine Learning? One of the most important questions when designing a Machine Learning inference and training procedure are the data input and output. Amino acid sequence of an arbitrary protein can be represented as a one-hot encoded vector, with every amino acid assigned a unique binary representation. As an example, let’s consider the amino acid sequence of , NS2B polypeptide from Zika Virus MGSSHHHHHHSSGLVPRGSHMTGKSVDMYIERAGDITWEKDAEVTGNSPRLDVALDESGDFSLVEEDGPPMRE The one-hot vector, which represents the NS2B Zika Virus sequence can be written as, np.array([0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], dtype=np.uint8) The output. Structural propensity data. Individual, residue-specific scores are bound between and . A propensity of implicates the sampling of beta-sheet conformations. A score of indicates behaviour found in disordered proteins, whereas is an indicator of properly folded alpha-helix. A score of should be understood as a situation when 50% of ensemble members form a helix and the remaining part samples different conformations. 1.0 3.0 1.0 2.0 3.0 2.5 The propensity score vector for is given by, NS2B polypeptide from Zika Virus np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.82, 1.70, 1.56, 1.43, 1.28, 1.59, 1.58, 1.77, 1.75, 2.06, 1.70, 1.95, 1.84, 2.17, 2.10, 2.16, 2.14, 2.21, 2.06, 2.06, 2.09, 2.07, 0.0, 0.0, 0.0, 2.02, 1.95, 1.96, 1.94, 1.97, 2.01, 2.06, 2.10, 2.14, 2.17, 2.13, 2.12, 2.05, 2.04, 2.03, 0.00, 2.09, 2.14, 2.16, 0.0, 2.16, 2.16, 2.16, 2.17], dtype=np.float32) Note: _0.0_ denotes missing experimental assignment data. Making it all interactive For the biology-oriented audience and curious computational scientists, we’ve created a web service at . https://peptone.io/dspp Use it to find proteins, explore their propensities and preview all the data in a machine learning-ready form. What’s next? We have made a model for development for you. boilerplate https://github.com/PeptoneInc/dspp-keras/tree/master/examples It is just a single Dense layer network. However, you should be able to add more layers and experiment with the best network architecture on your own. Call to action Feel free to ask questions about at dspp-keras support@peptone.io If you have found our useful, give us a Github repo STAR ✭. If you have further questions about structural propensity, feel free to leave comments under this article. References is based on ongoing scientific research into protein stability and intrinsic disorder. Therefore we suggest: dspp-keras Download and read the original research paper at http://biorxiv.org/content/early/2017/06/01/144840 Search, browse and download complete dSPP database at https://peptone.io/dspp Please cite us as, @article {Tamiola144840,	author = {Tamiola, Kamil and Heberling, Matthew Michael and Domanski, Jan},	title = {Structural Propensity Database Of Proteins},	year = {2017},	doi = {10.1101/144840},	publisher = {Cold Spring Harbor Labs Journals},	URL = {http://biorxiv.org/content/early/2017/06/01/144840},	eprint = {http://biorxiv.org/content/early/2017/06/01/144840.full.pdf},	journal = {bioRxiv}} Importantly, if you have decided to keep on reading, I strongly recommend the recent articles by which elegantly describe the issues with current protein biotechnology protocols. Matthew Heberling _Protein Science Deserves More from Big Data_blog.peptone.io Fame and Fundamentals Complicate Protein Biotech _Ever wonder whether protein structural variability could be packaged into a single score at the amino acid level? We…_blog.peptone.io Transforming big data to understand protein disorder: Insight into Zika Virus drug design About Peptone Founded in 2016 (Amsterdam, The Netherlands), offers state of the art solutions for protein biotechnology via Machine Learning and AI. We transform from public and private repositories into powerful predictive models and intuitive tools for protein production, stability, disorder, engineering, and directed evolution experiments, providing our clients with transparent and complementary software that saves time and yields precise research answers. Peptone big data

Hacking in silico protein engineering.

Liquidity provisioning in Uniswap V3 presents a stochastic optimal control problem with a well-defined utility function to maximize. This article introduces an innovative framework for intelligent liquidity provisioning, utilizing a combination of agent-based modeling and reinforcement learning. Our framework provides a robust and adaptive solution for optimizing liquidity provisioning strategies. The Uniswap V3 model mimics real-world market conditions, while the agent-based model (ABM) creates an environment for simulating agent interactions with Uniswap V3 pools. The reinforcement learning agent, trained using deep deterministic policy gradients (DDPG), learns optimal strategies, showcasing the potential of machine learning in enhancing DeFi participation. This approach aims to improve liquidity providers’ profitability and understanding of CFMM markets. Content Overview Introduction Intelligent liquidity provisioning framework Components of Intelligent Liquidity Provisioning Framework Agent-based simulator Reinforcement Learning Model Limitations Future Work Conclusion Resources References Introduction In my previous article on market making we explored the mechanics and strategies of market making in traditional financial markets. Building upon those insights, this article introduces an innovative framework for intelligent liquidity provisioning in the context of Uniswap V3. As mentioned in our prior research, our goal was to extend our understanding of market dynamics and liquidity management in decentralized finance ( , specifically through the development of the Intelligent Liquidity Provisioning Framework. [Market Making Mechanics and Strategies], DeFi) Decentralized finance (DeFi) has undergone remarkable growth, introducing innovative financial products and services accessible to a global audience. Uniswap V3, at the forefront of this innovation, has revolutionized liquidity provisioning with its concentrated liquidity feature. However, this advancement brings forth complex decision-making challenges for liquidity providers. This article introduces a comprehensive framework designed to address these challenges, offering a simulated environment for studying and optimizing liquidity provisioning strategies. Our framework comprises three key components: the Uniswap V3 model, an agent-based model (ABM), and a reinforcement learning agent. The Uniswap V3 model provides a representation of the pool, enabling the deployment and interaction with tokens and pools. The ABM introduces complexity by simulating agent interactions and market dynamics, creating a rich environment for strategy evaluation. The reinforcement learning agent, operating within this environment, adopts a deep deterministic policy gradient approach to learn and adapt strategies, aiming for optimal performance in liquidity provisioning. This research aims to develop an intelligent liquidity provisioning (ILP) mechanism using reinforcement learning (RL) to autonomously manage and optimize liquidity within the Uniswap V3 environment. The mechanism seeks to maximize the utility function, considering fees earned, impermanent loss, and other metrics based on liquidity providers’ preferences while adapting to the complex dynamics of the CFMM market. Intelligent Liquidity Provisioning Framework In the RL framework, the liquidity provisioning problem is formulated as a Markov Decision Process (MDP). The MDP consists of states, actions, and rewards. States represent the current market conditions, including asset prices, trading volumes, and other relevant variables. States: Actions correspond to the decisions made by the liquidity provider, such as adjusting liquidity allocations, rebalancing portfolios, etc. Actions: Rewards quantify the desirability of the outcomes based on the liquidity provider’s objective function, preferences, and constraints. The rewards can be positive for desirable outcomes (e.g., high returns) and negative for undesirable outcomes (e.g., high risk or underperformance). Rewards: The objective function represents the liquidity provider’s desired outcome, which can be a combination of factors like maximizing returns, minimizing risks, or achieving a specific trade-off between the two. Constraints can include limitations on liquidity allocations, capital utilization, risk tolerance levels, or other restrictions defined by the liquidity provider. Objective Function: RL training is an iterative process where the agent continuously updates its policy based on feedback. The agent learns from its experiences and refines its decision-making over time, gradually converging to more optimal liquidity provisioning strategies. Once the RL agent has been trained, it can be tested and evaluated using historical data or simulated environments to assess its performance against the liquidity provider’s objective function and constraints. The agent’s performance can be measured using metrics like returns, risk measures, or other relevant performance indicators. By applying RL algorithm, the liquidity provisioning mechanism can learn and adapt to changing market conditions, identify optimal liquidity provision strategies, and balance constraints and preferences specified by the liquidity provider. RL enables the mechanism to find solutions that maximize the liquidity provider’s objective function, considering various trade-offs and constraints autonomously and dynamically. Components of Intelligent Liquidity Provisioning Framework The framework comprises three major components: UniswapV3 Model The Uniswap V3 model implemented in Python offers a detailed and functional simulation of the Uniswap V3 protocol, capturing its nuanced mechanics and providing users with a comprehensive toolset for interacting with the protocol. The UniswapV3_Model class handles the deployment of tokens and pools, initializes pools, and provides an interface for pool actions and pool state retrieval. Overview The Uniswap Model serves as the foundation of the Intelligent Liquidity Provisioning Framework, encapsulating the core mechanics of Uniswap V3. It leverages compiled smart contracts from Uniswap’s V3-Core, deployed in a local Ganache environment using brownie, to create a realistic and interactive simulation. Contract Compilation and Deployment The framework integrates with Brownie, a Python-based development and testing framework for smart contracts, to compile and deploy the Uniswap V3 smart contracts. These contracts are then deployed to a local Ganache environment, providing a sandbox for testing and development. This setup ensures that users can interact with the Uniswap environment without the need for real assets or network transactions, fostering a safe and controlled experimentation space. Agent-Based Simulator is used to simulate the Uniswap V3 environment, agent policies are defined to incorporate the dynamics of Uniswap market participants. Different types of agents are used to simulate the dynamic Uniswap environment Tokenspice agent-based simulator Introduction simulates the actions and interactions of individual agents within the Uniswap V3 ecosystem. By modeling the complex behaviors of different participants, the ABM provides a comprehensive interface of Uniswap V3 dynamic environment, enabling the analysis and optimization of liquidity provisioning strategies. Tokenspice Agent-Based Model (ABM) Agent Types and Behaviors The ABM includes various agent types, each representing a specific role within the Uniswap V3 ecosystem. The two main agents are the Liquidity Provider Agent and the Swapper Agent, which interact with the Uniswap pools to provide liquidity and perform token swaps, respectively. The behavior of these agents is dictated by policies defined in the file, ensuring that their actions are aligned with real-world strategies and market conditions. agents_policies.py This agent adds and removes liquidity from the Uniswap pools. It follows a set of policies that dictate its actions based on the current state of the market and the agent’s preferences. Liquidity Provider Agent: The Swapper Agent performs token swaps within the Uniswap pools, taking advantage of price discrepancies and arbitrage opportunities. Its behavior is guided by policies that assess the potential profitability of trades, considering transaction fees and slippage. Swapper Agent: Simulation Configuration and Execution The file is central to the ABM, configuring how agents interact with each other and with the Uniswap pools. It defines the relationships between agents, policies, and the simulation environment. netlist.py The , , and modules provide the foundational elements for running simulations. The SimEngine orchestrates the simulation, managing the flow of time and the execution of agent actions. The SimStateBase maintains the current state of the simulation, storing data on agent holdings, pool states, and other relevant variables. The SimStrategyBase defines the overarching strategies that guide agent behavior throughout the simulation. SimEngine.py SimStateBase.py SimStrategyBase.py Reinforcement Learning Model Introduction The Reinforcement Learning (RL) Agent is a pivotal component of the Intelligent Liquidity Provisioning Framework, designed to interact with the Uniswap V3 ecosystem through Uniswap Model an agent-based model. This section delves into the RL Agent, its environment, and the DDPG (Deep Deterministic Policy Gradient) algorithm used for training. RL Agent Environment The RL Agent operates in a custom environment, , which interfaces with the Uniswap model and the agent-based model to simulate the DeFi market. This environment facilitates the agent’s interaction with Uniswap pools, allowing it to add and remove liquidity, and observe the consequences of its actions. The RL Agent interacts with the Uniswap model and ABM to simulate real-world liquidity provisioning in Uniswap V3. It chooses actions that result in adding or removing liquidity, with policies and simulation configuration defined in the ABM, ensuring realistic interactions. DiscreteSimpleEnv The environment’s state space includes various market indicators such as the current price, liquidity, and fee growth. These parameters are normalized and provided to the agent at each timestep. State Space: The agent’s action space consists of continuous values representing the price bounds for adding liquidity to a Uniswap pool. These actions are translated into interactions with the Uniswap pools, affecting the state of the environment. Action Space: The reward function is crucial for training the RL Agent. It takes into account the fee income, impermanent loss, portfolio value, and potential penalties, providing a scalar reward signal to guide the agent’s learning process. Reward Function: DDPG Agent The DDPG Agent is a model-free, off-policy actor-critic algorithm using deep function approximators. It can handle high-dimensional state spaces and continuous action spaces, making it well-suited for our Uniswap V3 environment. This network is responsible for providing the best-believed action given a state. It has a sigmoid output layer, outputting relative values for and which are then scaled to the desired range in agent env, representing the price bounds for adding liquidity. Actor Network: price_lower price_upper This network evaluates the action value function, estimating the expected return of taking an action in a given state. Critic Network: DDPG employs target networks for both the actor and critic, which are slowly updated to stabilize training. Target Networks: This technique is used to store a replay buffer of past experiences, allowing the agent to learn from a diverse set of samples, breaking correlations in observations, and smoothing out learning. Experience Replay: Interaction with Uniswap Model and ABM The RL Agent leverages the Uniswap model and agent-based model to simulate real-world liquidity provisioning in Uniswap V3. It interacts with the Uniswap pools through the , performing actions that result in adding or removing liquidity. The agent’s policies and the simulation configuration are defined in the ABM component, ensuring a realistic and coherent dynamic environment. DiscreteSimpleEnv The agent is trained over a series of episodes, each representing a different market scenario (different pool). The agent’s performance is evaluated based on its ability to maximize returns while minimizing risks associated with liquidity provisioning. The effectiveness of the Intelligent Liquidity Provisioning Framework is assessed through the evaluation of the reinforcement learning (RL) agent’s performance. Train and Evaluate Agent: To evaluate the RL agent, we set up a specialized evaluation environment, , which extends the base environment, . This environment is tailored for the evaluation of agent policies. Environment Setup: DiscreteSimpleEnvEval DiscreteSimpleEnv In our evaluation setup, we compare the RL agent’s performance against that of a baseline agent. The baseline agent’s actions are determined by a baseline policy that relies on the current state of the liquidity pool. This agent aims to provide a reference point for evaluating the RL agent’s performance. Baseline Agent: Results Training Evaluation Limitations Currently, the framework does not fully capture the real-time synchronization of pools, which can lead to discrepancies in modeling real Uniswap V3 dynamics. Future work should focus on incorporating mechanisms for better pool synchronization, potentially utilizing tick/position data or events to enhance realism. Pools Synchronization: The agent policies employed in the current framework are relatively simple and naive. To achieve more accurate simulations, future iterations should aim to define more comprehensive agent policies. These policies could model various types of Uniswap agents, such as noise traders, informed traders, retail liquidity providers, and institutional liquidity providers. Alternatively, statistical models trained on historical pool data can inform agent policies for more realistic behavior. Naive Agent Policies: The observation space provided to the agents lacks comprehensive information about the state of the pool. To improve decision-making capabilities, future enhancements should include tick and position data, along with engineered features that offer agents a more comprehensive understanding of the pool’s status. Sparse Observation Space: The action space for agents is currently constrained, with fixed liquidity amounts and restricted price range bounds. Expanding the action space to allow for more flexibility in liquidity provision, as well as considering multiple positions per step, can enhance the fidelity of the simulations. Limited Action Space: Future Work Agent Environment: Implement mechanisms to synchronize pools, possibly using tick/position data or events, to create more realistic dynamics in the Uniswap V3 environment. Synced Pools: Actor/Critic Network Architecture, alpha, beta, tau, batch size, steps, episodes, scaling parameters (rewards, actions, observation space) Hyperparameter Tuning: Define more sophisticated analytical policies that accurately model various Uniswap agents or utilize statistical models trained on historical pool data to inform agent behavior. Comprehensive Agent Policies: Enhance the observation space by including tick and position data, and engineer features that provide agents with a comprehensive view of the pool’s state. Informative Observation Space: Develop an improved reward function that accounts for a wider range of factors, leading to more effective agent training. Improved Reward Function: Instead of one position with a fixed budget at each timestep, implement a more comprehensive mechanism in which the agent is allocated a budget once at the start of the simulation and then learns to use this budget optimally in subsequent steps. Multiple Positions: Define more comprehensive baseline policies to evaluate the performance of the RL agent Baseline Policies: Agent Algorithm Further refine and optimize the hyperparameters of the reinforcement learning agent for better training performance. Hyperparameter Tuning: Explore alternative RL agent models, such as Proximal Policy Optimization (PPO) or Soft Actor-Critic (SAC), to determine if they offer advantages in specific scenarios. Experimentation with Other RL Agents: Investigate the application of multi-agent reinforcement learning techniques, which can be beneficial for modeling interactions among multiple liquidity providers and swappers. Multi-Agent RL (MARL): Implement online learning strategies that allow agents to adapt to changing market conditions in real time, providing a more dynamic and adaptive liquidity provisioning solution. Online Learning: Conclusion In the rapidly evolving landscape of decentralized finance (DeFi), liquidity provisioning plays a pivotal role in enabling efficient and secure trading. Uniswap V3, with its innovative concentrated liquidity feature, has pushed the boundaries of what is possible in DeFi liquidity management. However, the complexities of optimizing liquidity provisioning strategies within this dynamic ecosystem require innovative solutions. Our Intelligent Liquidity Provisioning Framework represents a significant step forward in addressing these challenges. By combining agent-based modeling and reinforcement learning, we have created a powerful toolkit for liquidity providers and market participants. This framework offers a robust and adaptive solution for optimizing liquidity provisioning strategies, with a focus on maximizing utility functions that encompass fees earned, impermanent loss mitigation, and other metrics tailored to individual preferences. Resources Github: https://github.com/idrees535/Intelligent-Liquidity-Provisioning-Framework-V1 References https://kth.diva-portal.org/smash/get/diva2:1695877/FULLTEXT01.pdf https://arxiv.org/pdf/2305.15821.pdf https://github.com/KodAgge/Reinforcement-Learning-for-Market-Making/tree/main https://arxiv.org/ftp/arxiv/papers/2211/2211.01346.pdf https://arxiv.org/pdf/2004.06985.pdf https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9682687 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0277042 https://deliverypdf.ssrn.com/delivery.php?ID=104119098102026014120072084014107007042068069003049020126088025087121115103007084028042013055035009000054122074096068089064070102052026003014069082076098016080066026088066039027093020006122067093104092065070020126069068106118079127088008098077106031120&EXT=pdf&INDEX=TRUE https://medium.com/blockapex/market-making-mechanics-and-strategies-4daf2122121c https://www.gauntlet.xyz/resources/uniswap-user-cohort-analysis https://gov.uniswap.org/t/uniswap-incentive-design-analysis/21662 https://arxiv.org/pdf/2108.07806.pdf https://www.researchgate.net/publication/341848292_Market_makers_activity_behavioural_and_agent_based_approach https://fruct.org/publications/volume-29/fruct29/files/Struc.pdf https://www.arxiv-vanity.com/papers/1911.03380/ https://insights.glassnode.com/the-week-onchain-week-31-2023/ Also published . here

A Detailed Framework for Intelligent Liquidity Provisioning in Uniswap V3 

Uniswap V3-এ বুদ্ধিমান তারল্য বিধানের জন্য একটি বিস্তারিত কাঠামো

Ein detailliertes Framework für die intelligente Liquiditätsbereitstellung in Uniswap V3

Un marco detallado para el aprovisionamiento inteligente de liquidez en Uniswap V3

Un cadre détaillé pour l'approvisionnement intelligent en liquidités dans Uniswap V3

Uniswap V3 में बुद्धिमान तरलता प्रावधान के लिए एक विस्तृत रूपरेखा

Uniswap V3 におけるインテリジェントな流動性プロビジョニングの詳細なフレームワーク

Uniswap V3의 지능형 유동성 프로비저닝을 위한 세부 프레임워크

Uma estrutura detalhada para provisionamento inteligente de liquidez no Uniswap V3

Подробная структура интеллектуального предоставления ликвидности в Uniswap V3

Uniswap V3'te Akıllı Likidite Tedariği için Ayrıntılı Bir Çerçeve

Khung chi tiết để cung cấp thanh khoản thông minh trong Uniswap V3

Uniswap V3 智能流动性配置的详细框架

The US (IARPA) is looking “to develop systems capable of modeling population movement patterns around the globe” using AI and sensors connected to the Internet of Things (IoT) and smart cities. Intelligence Advanced Research Projects Activity According to the Office of the Director of National Intelligence ( ), IARPA’s “Hidden Activity Signal and Trajectory Anomaly Characterization ( ) aims to establish ‘normal’ movement models across times, locations, and populations and determine what makes an activity atypical. ODNI HAYSTAC program “Expansive data from the Internet of Things and infrastructures provides opportunities to build new models that understand human dynamics at unprecedented resolution and creates the responsibility to understand privacy expectations for those moving through this sensor-rich world.” Smart City “This an unprecedented opportunity to understand how humans move, and HAYSTAC’s goal will be to build an understanding of what normal movement looks like at any given time and place” Dr. Jack Cooper, IARPA Leading the four-year research program is , who joined IARPA in 2020 after a stint at the National Geospatial-Intelligence Agency (NGA) in the Research Directorate, where he was a senior staff scientist for predictive analytics. HAYSTAC Dr. Jack Cooper For the program manager, represents “ .” HAYSTAC an unprecedented opportunity to understand how humans move, and HAYSTAC’s goal will be to build an understanding of what normal movement looks like at any given time and place https://www.youtube.com/watch?v=UDkCthIoc2M&embedable=true “With , we have the opportunity to leverage machine learning and advances in artificial intelligence to understand mobility patterns with exceptional clarity,” said Dr. Cooper in a statement to the ODNI. HAYSTAC “ ,” he added. The more robustly we can model normal movements, the more sharply we can identify what is out of the ordinary and foresee a possible emergency , “Current human mobility modeling techniques can provide high-level insight into human movement for the study of disease spread or population migration.” According to IARPA However, “They don’t provide the complex, fine-grained modeling the Intelligence Community (IC) needs to identify more subtle anomalies with confidence.” That’s where and Dr. Cooper come in. HAYSTAC “With HAYSTAC, we have the opportunity to leverage machine learning and advances in artificial intelligence to understand mobility patterns with exceptional clarity” Dr. Jack Cooper, IARPA Dr. Cooper is also the program manager for at least two other IARPA research programs focused on detecting and characterizing human activities, which include: ( ), which is using satellite imagery to detect, monitor, and characterize human construction projects, as well as natural processes like crop growth. Space-Based Machine Automated Recognition Technique SMART ( ), which is creating automatic activity detectors that can watch hours of video and highlight the few seconds when a person or vehicle does a specific activity (e.g., carry something heavy, load it into a vehicle, then drive away). Deep Intermodal Video Analytics DIVA “Internet of Thing devices are a growing source of data that can be collected to learn intent” Dr. Catherine Marsh, IARPA Speaking at the Department of Defense Intelligence Information System (DoDIIS) Worldwide Conference back in December, 2021, IARPA director the coming program when she said: Dr. Catherine Marsh foreshadowed HAYSTAC “Internet of Thing devices are a growing source of data that can be collected to learn intent. “Developing these new sensors and detectors, as well as thinking about clever ways to collect multi-modal data to reveal what our adversaries are attempting to hide from us, is at the very core of what our collection programs are aimed at doing.” For its program, IARPA has already awarded several contracts to big defense contractors and consulting firms with ties to academia, NGOs, and tech companies. HAYSTAC These contracts went to: Raytheon Technologies Research Center L3Harris Technologies, Inc. STR Kitware, Inc. Leidos, Inc. Novateur Research Solutions Deloitte Consulting LLP Raytheon BBN “As the systems mature, they will be evaluated based on probability of detection and false alarm performance in creating relevant alerts, ultimately seeking to identify 80% of anomalous activity while generating normal activity that is only 10% detectable,” according to the . HAYSTAC program description This article was originally published by Tim Hinchliffe on The Sociable.

IARPA's HAYSTAC Program: Predicting People's Movements with AI and IoT

IARPA-এর HAYSTAC প্রোগ্রাম: AI এবং IoT দিয়ে জনগণের আন্দোলনের পূর্বাভাস দেওয়া

Das HAYSTAC-Programm der IARPA: Vorhersage der Bewegungen von Menschen mit KI und IoT

Programa HAYSTAC de IARPA: predicción de movimientos de personas con IA e IoT

Programme HAYSTAC de l'IARPA : Prédire les mouvements des personnes avec l'IA et l'IoT

IARPA का HAYSTAC कार्यक्रम: AI और IoT के साथ जन आंदोलनों की भविष्यवाणी करना

IARPA の HAYSTAC プログラム: AI と IoT で人の移動を予測

IARPA의 HAYSTAC 프로그램: AI와 IoT를 통한 사람들의 움직임 예측

Programa HAYSTAC da IARPA: Prevendo os movimentos das pessoas com IA e IoT

IARPA'nın HAYSTAC Programı: Yapay Zeka ve Nesnelerin İnterneti ile İnsanların Hareketlerini Tahmin Etmek

Chương trình HAYSTAC của IARPA: Dự đoán chuyển động của mọi người bằng AI và IoT

IARPA 的 HAYSTAC 计划：利用人工智能和物联网预测人员流动

THE PHILOSOPHY OF THE STEAM-ENGINE

Section III.—Marine Engines

Artificial intelligence (AI) is rapidly transforming how businesses operate and interact with their customers. From automating routine tasks to transforming decision-making processes,  AI has the potential to significantly improve efficiency and effectiveness in the business world. In this article, I will discuss how AI is shaping the future of business and provide examples of some companies that are already implementing these technologies. AI for Automation Automation is likely to be the only major way AI will shape the future of business. This has many implications for automating business activities and affects a wide range of industries. The following are a few of the most common: 1. AI Chatbots Chatbots powered by AI are computer programs that simulate conversations with human users via text or audio. They can be used to automate customer service, answer questions, and even make recommendations, all of which were previously done by humans. Chatbots can also be used in marketing as they can be programmed to interact with customers and provide personalized services. IBM's Watson, Amazon's Lex, Google's Dialogflow, and Microsoft's Bot Framework are some of the most well-known and widely used chatbot service providers for millions of businesses. 2. Voice Recognition AI voice recognition software is also broadly used. Customer service is perhaps one of the most common applications of AI-based automation that is already in place. However, it is also widespread in voice-enabled search and navigation, controlling home automation systems, hands-free app control, and other applications. To give you an idea, Nuance's Dragon, SoundHound's Houndify, and wit.ai are some of the most extensively used voice recognition service providers. 3. Robotic Process Automation In addition to automating manual, repetitive, and time-consuming tasks, robotic process automation (RPA) products also sped up workflows considerably. Among the examples are data entry, document processing, and customer service interactions. Some of the largest companies applying it into practice are UI Path, WorkFusion, and Automation Anywhere. 4. Image Recognition Artificial Intelligence image recognition allows computers to recognize objects, scenes, and patterns in images by analyzing and interpreting them based on AI algorithms. This technology has several applications, including object detection and classification - which are typically done by humans - and facial recognition and image search. Amazon's Rekognition, Google's Vision API, and Clarifai are among the most popular and widely used image recognition services today. 5. Autonomous Mobility Autonomous mobility refers to the use of AI to enable vehicles to operate autonomously, without the need for a human driver. This technology has the potential to transform the way people and goods are transported, resulting in increased efficiency, safety, and accessibility. AI autonomous mobility is achieved through a combination of sensors, cameras, and other detectors that allow the vehicle to perceive its surroundings. The vehicle then uses AI algorithms to process this information and make decisions about how to navigate its environment, such as adjusting its speed, changing lanes, and avoiding obstacles. One of the primary advantages of AI autonomous mobility is enhanced safety. Autonomous vehicles have the potential to significantly reduce the number of road accidents by removing human error from the equation. Besides, by optimizing routes and lowering fuel consumption, the technology can help to reduce traffic congestion and improve overall driving efficiency. Another advantage of AI autonomous mobility is increased accessibility for people who cannot drive. Autonomous vehicles, in turn, can provide a new level of independence and mobility to the elderly, disabled, or those without a driver's license. This technology has been developed by Tesla, Waymo, Easy Mile, and Boston Dynamics, to name a few. AI for Data Analysis AI is also increasingly being used in data analysis to assist organizations in making better, faster, and more data-driven decisions. AI algorithms can process massive amounts of data and identify patterns and relationships that human analysts may not notice right away. This data can then be used to inform business strategies, improve operations, and stimulate innovation. Some of the more specialized applications for these tools encompass: 1. Predictive Analytics With the help of historical data, AI algorithms can predict events in the future. Using AI, companies can predict customer behavior, sales trends, and market conditions more accurately. A number of AI algorithms are embedded in Netflix and Spotify's algorithms to help the companies better understand their users' likes and dislikes. Based on this data, services can then provide relevant recommendations to their customers to keep them engaged by listening to music or watching series on those platforms. 2. Fraud Detection Large amounts of data can be analyzed by AI to detect fraudulent activities such as credit card fraud, insurance fraud, and cyberattacks. The technology can detect unusual patterns of behavior and flag them for further investigation, improving financial transaction security and protecting organizations from potential losses. Master Card has long used AI technologies to provide a fraud score for every transaction on its network as part of its anti-fraud efforts, making the information available to its issuers. Moreover, AI is used to detect fraud in combination with Mastercard's biometrics and behavioral analysis capabilities. 3. Predictive Maintenance Data from equipment and systems can be analyzed by AI algorithms to predict when they may fail. This enables businesses to take proactive measures to avoid downtime and lower maintenance costs. Every year, General Motors spends more than $1 billion on maintenance and $7 billion on capital improvements. To reduce maintenance costs and unplanned downtime, the company now uses AI-driven tools to support the widespread adoption of predictive maintenance across its over 140 global production sites[ ]. 1 4. Supply Chain Optimization AI algorithms can analyze supply chain data to identify inefficiencies and make recommendations for improvement. There are several applications for this today: to analyze data such as sales trends, consumer behavior, and market conditions to predict future demand, allowing companies to better plan their production and inventory levels. Demand forecasting – to optimize inventory management by lowering the risks of stockouts and overstocking. Inventory management - to optimize delivery routes and reduce transportation costs by analyzing data such as traffic conditions, delivery routes, and shipping costs. Delivery optimization - to provide real-time visibility into shipment status, allowing businesses to quickly identify and resolve any issues that may arise. Supply chain visibility - to analyze data such as consumer demand, shipping costs, and market conditions in order to optimize product distribution while lowering costs and increasing margins. Blue Yonder, Aera Technology, Kinaxis, and IBM Watson Supply Chain are a few of the most recognizable companies in the supply chain optimization area. Product distribution - AI for Customer Service AI is being utilized more and more to improve customer service and provide a better customer experience. Artificial intelligence technology can assist organizations in increasing the efficiency and effectiveness of their customer service operations while also providing customers with timely and personalized support. 1. Virtual Assistants AI virtual assistants can provide personalized support to customers, assisting them in finding information or completing tasks. Virtual assistants, for example, can be integrated into websites or mobile apps to assist customers with their shopping experience. As such, Walmart customers can ask Google Assistant to add specific items to their shopping carts or learn more about their shopping habits in the app[ ]. 2 2. Sentiment Analysis AI can be used to understand customer sentiments and opinions by analyzing customer feedback such as social media posts and survey responses. This data can assist organizations in identifying areas for improvement and providing better customer service. For example, the global FMCG company Mars utilizes tools to assess video content and analyze sentiment in online ratings and reviews[ ]. 3 3. Automated Voice Response AVR tools are used to provide automated customer service over the phone and interact with customers by providing audio responses to their questions or requests. These systems can be programmed to respond to simple questions and requests, provide callers with information such as current balances, allow customers to access specific account information, and, when necessary, redirect customers to an operator or customer service representative. Alvaria, Avaya, and GeneSys are three of the most recognized companies in this field. AI for Marketing AI is being used to increase the efficiency of marketing efforts in a variety of ways. AI technology can assist organizations in better understanding their customers, boosting campaign targeting, and measuring the results of their efforts. It is used in marketing in the following ways: 1. Customer Segmentation To create targeted marketing campaigns, AI algorithms can analyze customer data such as demographic information, purchase history, and social media behavior. This enables businesses to better understand their customers and increase the effectiveness of their marketing efforts. The Amazon Personalize automated segmentation system, which matches customers with the most compatible products tailored to their personal needs, is one of the most vivid examples. This is achieved through the use of special techniques that learn how users interact with items and then segment them based on their preferences. 2. Social Media Marketing To better understand customer sentiment and preferences, AI algorithms can analyze social media data such as posts, comments, and likes. This data can be used to boost social media marketing efforts, such as by creating more relevant and engaging content. Content optimization By analyzing data to uncover patterns and trends in user behavior, this technology automates the process of personalizing content to target individual users. AI-powered content optimization tools can assist marketers in creating more personalized, compelling content to engage and convert customers by understanding user preferences. This technology also assists marketers in optimizing content for various channels such as the web, mobile browsers, apps, and others. Some of the most well-known companies in this regard are Marketo Engage, BridgtEdge, and Evergage. AI for Legal work In the legal industry, Artificial Intelligence is being used to automate repetitive tasks, improve the accuracy of legal research, and provide more efficient and cost-effective services to clients. 1. Contract Review and Analysis AI algorithms can be used to review and analyze large volumes of contracts, assisting lawyers in identifying key provisions and clauses in a timely manner. When compared to manual review, this can save time and improve the accuracy of contract review. 2. Document discovery AI algorithms can be used to categorize and prioritize legal documents, such as those used in litigation discovery. This can help lawyers identify relevant documents more quickly and make more informed decisions. 3. Legal Research Legal research tools powered by AI, such as Westlaw and LexisNexis, can provide lawyers with timely and accurate information on legal precedents, regulations, and case law. This can assist them in conducting more effective legal research and providing more informed advice to clients. 4. Document Automation Artificial intelligence algorithms can be used to automate the creation of legal documents like contracts and agreements. When compared to manual drafting, this can improve the efficiency and accuracy of document creation. Kira, Ross, Cognitiv+, LawGeex, Luminance, and Legal Robot are some of the best-known tools/companies developing and selling these technologies today. AI for Creative Activities Artificial intelligence is being used in the creation of music and video to automate tasks, generate new content, and provide artists and content creators with new creative tools. 1. Music Composition AI algorithms can generate new music by either creating original compositions or recombining existing elements in novel ways. For example, AI can analyse existing music to determine the key elements of various genres and then use this knowledge to create new pieces. 2. Video Editing AI algorithms can be used to edit video footage automatically, such as selecting the best shots, removing background noise, and color correcting. In comparison to manual editing, this can save time and improve the accuracy of video editing. 3. Content Creation AI algorithms can be used to create new types of content, such as computer-generated imagery (CGI) or motion graphics. This can provide new tools for artists and content creators to create and experiment with new forms of content. 4. Music Production AI algorithms can be used to automate music production tasks like mixing and mastering. AI algorithms, for example, can be used to optimise the balance of different audio elements in a mix, thereby improving the overall sound quality of a recording. 5. Video Enhancement AI algorithms can be used to improve video content quality by removing noise, stabilizing shaky footage, and increasing resolution. This can provide tools for content creators to produce high-quality video content with less effort. Amper Music, AIVA, Flow Machines, Synthesia, Elai, and DaVinci Resolve are among the most well-known companies in this field. AI to Identify Customer Needs Last but not least, Artificial intelligence is being used to identify customer needs by analyzing massive amounts of customer data and behavior in order to gain insights into customer preferences, motivations, and needs. One of the most visible examples of this is currently taking place in the fashion industry, with some examples provided below: This online personal styling service recommends clothing items to customers based on their personal style, body type, and preferences using AI algorithms. Stitch Fix: To determine which styles and colors are most popular, the global fashion retailer uses AI to analyze data from sales, social media, and customer feedback. The data is then used to inform their design and production decisions. Zara: This online clothing rental service employs Artificial Intelligence to recommend styles to customers based on previous rentals, preferences, and events they are attending. Rent the Runway: To inform their design and production decisions, this global fashion brand employs AI to analyze consumer behavior and preferences, such as the most popular colors, styles, and materials. Tommy Hilfiger: An Indian online fashion and lifestyle retailer employs AI to recommend products to customers based on their browsing and purchasing history, as well as their preferences and style. Myntra: Conclusion To summarise everything mentioned above, Artificial Intelligence is rapidly transforming the business world and shaping the future of many industries. AI is assisting businesses in making more informed decisions, enhancing efficiency and productivity, and providing a more personalized and effective customer experience by processing and analyzing massive amounts of data. AI is used in a variety of applications, including data analysis, marketing, customer service, and legal work. In a rapidly changing business landscape, the use of AI in these areas is aiding businesses in gaining a competitive advantage and staying ahead of the curve.
Still, despite the numerous benefits of AI, there are concerns about its potential impact on the workforce, as well as on privacy and security. As AI technology evolves, it will be crucial for companies to consider these issues and take a responsible and ethical approach to AI use. Overall, AI is profoundly and far-reaching shaping the future of business, and businesses that can effectively harness its power will be well-positioned to succeed in the years ahead. Lead image developed by stable diffusion.

AI's Growing Influence and the Future of Business

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