Trending November 2023 # Future Of Trading In Next Generation # Suggested December 2023 # Top 11 Popular

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The EU’s e-Privacy law, which is requested in 2023, is defined to be the upcoming key item of legislation designed to safeguard the privacy and safety of private details.

Obviously, people in the finance industry are not any different to people in almost any other sector.

Legislation to the Real-world

However, legislation composed even a few years ago is trying hard to deal with protecting privacy on these new platforms.

To an elderly professional it might look like the brand new e-Privacy law is 1 step ahead of trading tendencies by capturing all eCommerce information (such as metadata). However, the reality is that financial companies are in reality often a step behind in the execution and ennoblement of eCommerce and security of their eCommerce data. Its time to the monetary industry to think beforehand!

Recognizing eCommerce

There are good reasons why many dealers have turned into wider eCommerce and messaging stations.

These stations are quick to use, immediately connecting with the ideal person in just about any location by means of a private device. Contrary to waiting for an email reply by way of instance, they provide visibility of this message being received and a response is sent. At extremely time-critical surroundings this makes great sense.

Most companies have their company emails and onsite voice communications securely stored and tracked in the event of a discrepancy or analysis.

It would be quite simple to insist no additional stations are used — but the truth is that the dealers may use these channels for communicating and companies will need to align with this.

The other strategy could be equally damaging. Unregulated and uncontrolled utilization of societal and cellular communications may leave the business at severe risk of information breaches and following regulatory scrutiny/punishment.

The penalties may be eye-watering. At the biggest single circumstance, a company has been fined $2 million for failing to employ a reasonable supervisory system to reassess emails.

The dangers can be harder to control whether the organisation permits traders a degree of BYOD (bring your own device) flexibility within their own role. How do a financial company protect against privacy or data breaches on a stage and device it does not directly control?

Embracing digital transformation

Certainly, neither prohibiting eCommerce utilize or turning a blind eye on its use by dealers are sensible strategies for almost any contemporary financial company. The sensible strategy is to adopt this electronic transformation and also to take possession of it.

Many young dealers and clients wish to use the hottest eCommerce to match their tastes, so companies will need to make sure that this is enabled but additionally that apparatus and eCommerce stations are correctly monitored and controlled. Assessing all eCommerce information is properly gathered, securely kept and readily available for reports and investigation in a minute’s notice guarantees any breaches (or possible breaches) could be addressed promptly.

Meeting fresh e-Privacy Regulations

This strategy will be more significant when the new EU e-Privacy Legislation comes into power. Interestingly, however, the new laws will also pay for the solitude of the dealers themselves, in addition to customers and the company.

Fines for Indices will probably about the very same amounts as GDPR (around $20 million or around four percent of global yearly turnover, whichever is the greatest ) — significantly severe enough to induce companies financial hardship in addition to reputation damage.

Giving the Men and Women what they need

It’s critical to first comprehend the growth of eCommerce and then to use the ideal Reg Tech solutions to make sure your company stays at the very front of the shift, instead of being left behind with it.

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Experience The Power Of Langchain: The Next Generation Of Language Learning

LangChain is an innovative framework for creating language-powered applications. LangChain, with its configurable approach and extensive integration features, gives developers a new level of control and flexibility when it comes to exploiting language models.

LangChain

LangChain is a framework for creating language-powered apps. The most powerful and distinct apps will not only use an API to access a language model, but will also:

Be data-aware: Connect a language model to additional data sources.

Be agentic: Permit a language model to interact with its surroundings.

The LangChain framework is designed with the above principles in mind. This is the Python specific portion of the documentation.

Prompts:

At a high level, prompts are organized by use case inside the prompts directory. To load a prompt in LangChain, you should use the following code snippet:

from langchain.prompts import load_prompt prompt = load_prompt('lc://prompts/path/to/file.json')

Chains

Chains extend beyond a single LLM call to include sequences of calls (to an LLM or another utility). LangChain offers a standard chain interface, numerous connections with other tools, and end-to-end chains for typical applications.

Chains are organized by use case inside the chains directory at a high level. Use the following code snippet to load a chain in LangChain:

from langchain.chains import load_chain chain = load_chain('lc://chains/path/to/file.json')

Agents

Gents involve an LLM making judgements on which Actions to do, performing that Action, observing an Observation, and repeating this process until completed. LangChain provides a standard agent interface, a collection of agents, and examples of end-to-end agents.

Agents are organized by use case inside the agents directory at a high level. Use the following code snippet to load an agent in LangChain:

from langchain.agents import initialize_agent llm = ... tools = ... agent = initialize_agent(tools, llm, agent="lc://agents/self-ask-with-search/agent.json") Installation

To get started, install LangChain with the following command:

pip install langchain # or conda install langchain -c conda-forge Environment Setup

Integrations with one or more model providers, data storage, APIs, and so on are frequently required when using LangChain.

Because we will be using OpenAI’s APIs in this example, we must first install their SDK:

pip install openai

We will then need to set the environment variable in the terminal.

export OPENAI_API_KEY="..."

Alternatively, you could do this from inside the Jupyter notebook (or Python script):

import os os.environ["OPENAI_API_KEY"] = "..." Building a Language Model Application: LLMs

We can begin developing our language model application now that we have installed LangChain and configured our environment.

LangChain has a number of modules that may be used to create language model applications. Modules can be integrated to make more complicated applications or used alone to construct basic apps.

LLMs: Get predictions from a language model

The most fundamental LangChain building component is calling an LLM on some input. Let’s go over an easy example of how to achieve this. Assume we’re creating a service that generates a company name based on what the company produces.

To achieve this, we must first import the LLM wrapper.

from chúng tôi import OpenAI

We can then initialize the wrapper with any arguments. In this example, we probably want the outputs to be MORE random, so we’ll initialize it with a HIGH temperature.

llm = OpenAI(temperature=0.9)

We can now call it on some input!

text = "What would be a good company name for a company that makes colorful socks?" print(llm(text)) Feetful of Fun Prompt Templates: Manage prompts for LLMs.

Calling an LLM is a good start, but it’s only the beginning. When you utilise an LLM in an application, you usually do not pass user input directly to the LLM. Instead, you’re presumably gathering user input and creating a prompt, which you then send to the LLM.

In the last example, for example, the text we provided in was hardcoded to request a name for a firm that sold colourful socks. In this hypothetical service, we’d like to take only the user input describing what the company does and format the prompt with that information.

This is simple with LangChain!

Let’s start with the prompt template:

from langchain.prompts import PromptTemplate prompt = PromptTemplate( input_variables=["product"], template="What is a good name for a company that makes {product}?", )

Let’s now see how this works! We can call the .format method to format it.

print(prompt.format(product="colorful socks")) What is a good name for a company that makes colorful socks? Chains: Combine LLMs and prompts in multi-step workflows

Until now, we’ve only used the PromptTemplate and LLM primitives on their own. Of course, a real application is a combination of primitives rather than a single one.

In LangChain, a chain is built up of links that can be primitives like LLMs or other chains.

An LLMChain is the most basic sort of chain, consisting of a Prompt Template and an LLM.

Extending on the previous example, we can build an LLMChain that accepts user input, prepares it with a Prompt Template, and then sends the processed result to an LLM.

from langchain.prompts import PromptTemplate from chúng tôi import OpenAI llm = OpenAI(temperature=0.9) prompt = PromptTemplate( input_variables=["product"], template="What is a good name for a company that makes {product}?", )

We can now create a very simple chain that will take user input, format the prompt with it, and then send it to the LLM:

from langchain.chains import LLMChain chain = LLMChain(llm=llm, prompt=prompt)

Now we can run that chain only specifying the product!

chain.run("colorful socks")

The first chain is an LLM Chain. Although this is one of the simpler types of chains, understanding how it works will prepare you for working with more complex chains.

Agents: Dynamically Call Chains Based on User Input

So far, the chains we’ve looked at run in a predetermined order.

An LLM is no longer used by agents to choose which actions to do and in what order. An action might be examining the output of a tool or returning to the user.

Agents may be immensely strong when utilized appropriately. In this tutorial, we will demonstrate how to utilize agents using the simplest, highest-level API.

In order to load agents, you should understand the following concepts:

Tool: A function that serves a certain purpose. This can include Google Search, database lookups, Python REPLs, and other chains. A tool’s interface is presently a function that is meant to take a string as input and return a string as output.

LLM: The language model that drives the agent.

Agent: The agent to use. This should be a string containing the name of a support agent class. This notebook only covers utilizing the standard supported agents because it focuses on the simplest, highest-level API. See the documentation on custom agents if you wish to implement one.

Agents: For a list of supported agents and their specifications, see here.

Tools: For a list of predefined tools and their specifications, see here.

For this example, you will also need to install the SerpAPI Python package.

pip install google-search-results

And set the appropriate environment variables.

import os os.environ["SERPAPI_API_KEY"] = "..."

Now we can get started!

from langchain.agents import load_tools from langchain.agents import initialize_agent from langchain.agents import AgentType from chúng tôi import OpenAI # First, let's load the language model we're going to use to control the agent. llm = OpenAI(temperature=0) # Next, let's load some tools to use. Note that the `llm-math` tool uses an LLM, so we need to pass that in. tools = load_tools(["serpapi", "llm-math"], llm=llm) # Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use. agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True) # Now let's test it out! agent.run("What was the high temperature in SF yesterday in Fahrenheit? What is that number raised to the .023 power?")

I need to find the temperature first, then use the calculator to raise it to the .023 power. Action: Search Action Input: "High temperature in SF yesterday" Observation: San Francisco Temperature Yesterday. Maximum temperature yesterday: 57 °F (at 1:56 pm) Minimum temperature yesterday: 49 °F (at 1:56 am) Average temperature ... Thought: I now have the temperature, so I can use the calculator to raise it to the .023 power. Action: Calculator Action Input: 57^.023 Observation: Answer: 1.0974509573251117

Thought: I now know the final answer Final Answer: The high temperature in SF yesterday in Fahrenheit raised to the .023 power is 1.0974509573251117.

> Finished chain.

Memory: Add State to Chains and Agents

All of the chains and agents we’ve encountered so far have been stateless. However, you may want a chain or agent to have some concept of “memory” in order for it to remember information from previous interactions. When designing a chatbot, for example, you want it to remember previous messages so that it can use context from that to have a better conversation. This is a sort of “short-term memory.” On the more complex side, you could imagine a chain/agent remembering key pieces of information over time – this would be a form of “long-term memory”.

LangChain provides several specially created chains just for this purpose. This notebook walks through using one of those chains (the ConversationChain) with two different types of memory.

By default, the ConversationChain has a simple type of memory that remembers all previous inputs/outputs and adds them to the context that is passed. Let’s take a look at using this chain (setting verbose=True so we can see the prompt).

from langchain import OpenAI, ConversationChain llm = OpenAI(temperature=0) conversation = ConversationChain(llm=llm, verbose=True) output = conversation.predict(input="Hi there!") print(output)

Prompt after formatting: The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

Current conversation:

Human: Hi there! AI:

> Finished chain. ‘ Hello! How are you today?’

output = conversation.predict(input="I'm doing well! Just having a conversation with an AI.") print(output)

Current conversation:

Human: Hi there! AI: Hello! How are you today? Human: I’m doing well! Just having a conversation with an AI. AI:

> Finished chain. ” That’s great! What would you like to talk about?”

Building a Language Model Application: Chat Models

Similarly, conversation models can be used instead of LLMs. Language models are a subset of chat models. While chat models use language models behind the scenes, the interface they expose is slightly different: instead of exposing a “text in, text out” API, they expose an interface where “chat messages” are the inputs and outputs.

Because chat model APIs are still in their early stages, they are still determining the appropriate abstractions.

Get Message Completions from a Chat Model

You can get chat completions by passing one or more messages to the chat model. The response will be a message. The types of messages currently supported in LangChain are AIMessage, HumanMessage, SystemMessage, and ChatMessage – ChatMessage takes in an arbitrary role parameter. Most of the time, you’ll just be dealing with HumanMessage, AIMessage, and SystemMessage.

from langchain.chat_models import ChatOpenAI from langchain.schema import ( AIMessage, HumanMessage, SystemMessage ) chat = ChatOpenAI(temperature=0)

You can get completions by passing in a single message.

chat([HumanMessage(content="Translate this sentence from English to French. I love programming.")])

You can also pass in multiple messages for OpenAI’s gpt-3.5-turbo and gpt-4 models.

messages = [ SystemMessage(content="You are a helpful assistant that translates English to French."), HumanMessage(content="Translate this sentence from English to French. I love programming.") ] chat(messages)

You can go one step further and generate completions for multiple sets of messages using generate. This returns an LLMResult with an additional message parameter:

batch_messages = [ [ SystemMessage(content="You are a helpful assistant that translates English to French."), HumanMessage(content="Translate this sentence from English to French. I love programming.") ], [ SystemMessage(content="You are a helpful assistant that translates English to French."), HumanMessage(content="Translate this sentence from English to French. I love artificial intelligence.") ], ] result = chat.generate(batch_messages) result

You can recover things like token usage from this LLMResult:

result.llm_output['token_usage'] Chat Prompt Templates

Similar to LLMs, you can make use of templating by using a MessagePromptTemplate. You can build a ChatPromptTemplate from one or more MessagePromptTemplates. You can use ChatPromptTemplate’s format_prompt – this returns a PromptValue, which you can convert to a string or Message object, depending on whether you want to use the formatted value as input to an llm or chat model.

For convenience, there is a from_template method exposed on the template. If you were to use this template, this is what it would look like:

from langchain.chat_models import ChatOpenAI from chúng tôi import ( ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate, ) chat = ChatOpenAI(temperature=0) template = "You are a helpful assistant that translates {input_language} to {output_language}." system_message_prompt = SystemMessagePromptTemplate.from_template(template) human_template = "{text}" human_message_prompt = HumanMessagePromptTemplate.from_template(human_template) chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt]) # get a chat completion from the formatted messages chat(chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_messages()) Chains with Chat Models

The LLMChain discussed in the above section can be used with chat models as well:

from langchain.chat_models import ChatOpenAI from langchain import LLMChain from chúng tôi import ( ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate, ) chat = ChatOpenAI(temperature=0) template = "You are a helpful assistant that translates {input_language} to {output_language}." system_message_prompt = SystemMessagePromptTemplate.from_template(template) human_template = "{text}" human_message_prompt = HumanMessagePromptTemplate.from_template(human_template) chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt]) chain = LLMChain(llm=chat, prompt=chat_prompt) chain.run(input_language="English", output_language="French", text="I love programming.") Agents with Chat Models

Agents can also be used with chat models; you can initialize one using AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION as the agent type.

from langchain.agents import load_tools from langchain.agents import initialize_agent from langchain.agents import AgentType from langchain.chat_models import ChatOpenAI from chúng tôi import OpenAI # First, let's load the language model we're going to use to control the agent. chat = ChatOpenAI(temperature=0) # Next, let's load some tools to use. Note that the `llm-math` tool uses an LLM, so we need to pass that in. llm = OpenAI(temperature=0) tools = load_tools(["serpapi", "llm-math"], llm=llm) # Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use. agent = initialize_agent(tools, chat, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True) # Now let's test it out! agent.run("Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?") > Entering new AgentExecutor chain... Thought: I need to use a search engine to find Olivia Wilde's boyfriend and a calculator to raise his age to the 0.23 power. Action: { "action": "Search", "action_input": "Olivia Wilde boyfriend" } Observation: Sudeikis and Wilde's relationship ended in November 2023. Wilde was publicly served with court documents regarding child custody while she was presenting Don't Worry Darling at CinemaCon 2023. In January 2023, Wilde began dating singer Harry Styles after meeting during the filming of Don't Worry Darling. Thought:I need to use a search engine to find Harry Styles' current age. Action: { "action": "Search", "action_input": "Harry Styles age" } Observation: 29 years Thought:Now I need to calculate 29 raised to the 0.23 power. Action: { "action": "Calculator", "action_input": "29^0.23" } Observation: Answer: 2.169459462491557 Thought:I now know the final answer. Final Answer: 2.169459462491557 > Finished chain. '2.169459462491557' Memory: Add State to Chains and Agents

Memory may be used with chains and agents that have been initialized using conversation models. The primary difference between this and Memory for LLMs is that instead of condensing all previous messages into a string, we may store them as their own distinct memory object.

from langchain.prompts import ( ChatPromptTemplate, MessagesPlaceholder, SystemMessagePromptTemplate, HumanMessagePromptTemplate ) from langchain.chains import ConversationChain from langchain.chat_models import ChatOpenAI from langchain.memory import ConversationBufferMemory

prompt = ChatPromptTemplate.from_messages([ SystemMessagePromptTemplate.from_template("The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know."), MessagesPlaceholder(variable_name="history"), HumanMessagePromptTemplate.from_template("{input}") ])

llm = ChatOpenAI(temperature=0) memory = ConversationBufferMemory(return_messages=True) conversation = ConversationChain(memory=memory, prompt=prompt, llm=llm)

conversation.predict(input="Hi there!")

conversation.predict(input="I'm doing well! Just having a conversation with an AI.")

conversation.predict(input="Tell me about yourself.")

How Jumping Genes Hijack Their Way Into The Next Generation Of Babies

As we all learned in health class, when a baby animal is created, genetic material from two biological parents combines to create a new being—one with some genes from each parent. What you may not know is that a third genetic element is involved in this process, a hitchhiker whose existence and self-propagation may be essential to life as we know it.

Transposon, or transposable element, is the scientific name for these hitchhikers lurking in our genome. These DNA sequences are able to move around within the genome and replicate themselves, sometimes with negative consequences for their hosts. Transposon-related mutations have been blamed for hemophilia and some kinds of cancer. But research over the past decade has revealed that our relationship with these elements, which make up a large percentage of the human genome, is much more complex than previously thought. The mutations caused by transposons’ presence and movements have also shaped evolution over the millennia. Until now, however, nobody had looked at the question of how transposons manage to incite this change by hitchhiking into the next generation after conception.

For the first time, new research has shown the kinds of cells that transposons target in order to “jump” into the future with embryos who will develop into new beings. Understanding this process will let us understand more about the transposons’ function and relationships. To explore this question, Zhao Zhang and his team at the Carnegie Institution for Science relied on the oft-studied fruit fly.

In theory, if transposons were allowed to run unchecked in the body, they’d result in so many genetic errors that we’d simply die. But somewhere along the way, animals developed a defensive strategy: a set of RNA molecules that limit the ability of the transposons to, well, transcribe themselves. Although transposons sometimes manage to slip past these defenses, known as piRNA, the genome is reasonably stable, with the transposons staying put and not transposing all that often.

That makes it difficult to track when they do transpose, specifically into the cells that create the next generation—a question that had never been asked before in any case, says Zhang.

“For our study what we were trying to do is reach single-cell resolution,” he says—that is, track how transposons moved through cells on an individual basis rather than find their presence in a piece of tissue that has many different kinds of cells in it. To do this, they turned off a specific kind of piRNA and watched how the jumping genes moved as the egg developed from two germ cells (one from each parent).

Jumping genes, which mobilize around the genome, use nurse cells to manufacture invading products that preferentially integrate into the genome of developing egg cells, called oocytes. Zhao Zhang

They found that some jumping genes—known as retrotransposons—rely on “nurse cells” that produce genetic supplies like proteins and RNA for the developing egg. They tag along with some of those supplies into the egg, where they transpose themselves into the egg DNA hundreds or even thousands of times.

This research offers new insights into the strange world of transposons and how they have made themselves such a lasting part of our evolution. “It reveals the complex life of transposons,” says Cornell University molecular biologist Cedric Feschotte, who was not involved with this study. There’s more work to do, of course, but the new research reveals an elegant strategy that these genetic hitchhikers use to keep on heading down the road.

Envisioning The Future With Computational Design: Next 3.0

THE PAST, PRESENT & FUTURE: An Online Interactive Conference with global frontiers on computational design.

Computational Design: Next 3.0 was a two-day interactive conference on the 23rd and 24th January 2023 with global frontiers and design thinkers collaborating on a single platform for live presentations, tutorials, interactive sessions, live mentorship, and panel discussions.

Computational Design: NEXT 3.0

Day 1

CD Next 3.0 took another greater step to delve the audience into an immersive and intriguing journey of experiencing computational design like never before. A 2-day conference from 23-24th January 2023, with the ultimate product mix of workshops, panel discussions, tech demonstrations, and discussions.

Arturo Tedeschi on the future of computational design with a comparative study and a captivating presentation.

Design head of rat [LAB] studio – Sushant Verma on implementation of computational design skills in large-scale projects

Followed by the Design entrepreneur, architect, computational designer, and educator – co-founder and design head of rat [LAB] studio – Sushant Verma with his insights on some of his works and implementation of computational design skills in large-scale projects by merging ecology and parametric architecture.

Michael Pryor, Design direction at Design Morphine on computational design and coding as a professional practice.

Michael Pryor, Computational designer at Nike NXT Digital innovation, Design direction at Design Morphine, and Author of Pufferfish Grasshopper3d Plug-in then discussed computational design and coding as a professional practice.

Volkan Alkanoglu showcasing his works that integrate technology

An insightful Q&A Session with Volkan Alkanoglu

Workshop by Cherylene Shangpliang, Architect at Zaha Hadid Architects on Maya + Enscape

Q&A Session with Cherylene Shangpliang, Architect at Zaha Hadid Architects

The keynote lecture was followed by an interactive and insightful Guest workshop by Cherylene Shangpliang, Architect at Zaha Hadid Architects on Maya + Enscape. It was a demonstrative workshop on Computational design in achieving an end product within 90 minutes including generic interface know-how, key points and shortcuts, and first-hand experience in creating complex geometries.

Moritz Waldemeyer on playful experimentations

Later, Moritz Waldemeyer, the British/German designer and engineer presented his philosophy of playful experimentation by forging links between technology, art, fashion, and design. He extensively talked about his experimentations in the fields of lighting design and digital art, along with his wide range of collaborations with the music industry. An eccentric presentation that captivated the audience’s attention towards the infinite possibility that computational design brings to the table.

Daniel Caven with an engaging workshop on Grasshopper 3D

After a string of an innovative palette of architects and design thinkers, Daniel Caven, an experimental design architect from Daniel Caven Design curated a workshop on Grasshopper 3D. The workshop was focused on exploring the boundaries of Grasshopper to realize it as one of the pivotal game-changers in the fields of computational design. An engaging workshop with simplistic steps on learning to add complexity like curves, geometries while collaborating with Lumion, were some of the useful insights. Learning software from a stalwart who created the first 3d printed modular home in itself was a captivating experience.

Niccolo Casas on the Emergence of digital technologies and additive manufacturing in architecture and Fashion

The engaging workshop was followed by a guest lecture by Niccolo Casas, Italian architect, professor, principal, and founder of Nicola Casas architecture where he discussed the Emergence of digital technologies and additive manufacturing in architecture and Fashion. The endnote was done by Hamid Hassanzadeh, Founder of PA concluding day 1 and updating the audience with the list of events for Day 2.

Day 2

Day 2 included a wide array of speakers, workshops, walkthroughs, keynote lectures, and Q&A sessions.

A collaborative cross-platform workshop that shuffles Computational Design methods

Michael Pryor, Design Director & Computational Designer, DesignMorphine & Sushant Verma, Co-founder & Design Head, rat[LAB] Studio ran a collaborative cross-platform workshop that shuffles Computational Design methods from one technique to another, integrating to build a Bridge through multiple techniques of computational overlaps.

A collaborative cross-platform workshop that shuffles Computational Design methods

Each tutor built this program on rational decision-making aided by computational tools, right from seating design, sightline optimization, structural skin design, environmental design integration, panelization, etc. as a part of an integrated and collaborative process using Grasshopper3d + MAYA. Comprehensive knowledge on 3 different computational software was curated in a session of 45 minutes covering the multiple techniques with an interactive narration to ease out the learning process.

Aldo Sollazzo showcased his works and studio methodology

Aldo Sollazzo, architect, researcher, and founder of Noumena and Fab Lab Frosinone showcased his works and studio methodology by highlighting some unknown points about Lumion. An expert in computational design and digital fabrication, he has been director and coordinator of the RESHAPE digital craft community, a platform promoting the development and implementation of innovative ideas from the world of digital design and fabrication. He also emphasized implementing innovative solutions in the fields of Robotics, 3D printing, and Wearable Tech.

A walkthrough presented by ILLUSORR taking virtual reality to a newfound level

Accounting as some of the most eccentric parts of CD Next 3.0, a walkthrough was presented by ILLUSORR taking virtual reality to a newfound level through an online session. ILLUSORR is the world’s first design-oriented virtual platform. The platform ILLUSORR is providing will introduce the world to the latest frontiers of design, and provide a completely immersive experience that will include; fashion shows, exhibitions, showrooms, virtual stores, workshops, competitions, talks, and even concerts! It is a platform that will allow virtual gatherings and interactions, using uniquely designed avatars, and moving beyond the 2d-webcam configuration that has become custom.

A dynamic workshop on Grasshopper 3D Plugins by James Dalessandro

An immersive walkthrough was followed by a dynamic workshop on Grasshopper 3D Plugins by James Dalessandro, computational designer and Co-Founder and Director at Emergent Design, a design technology company focused on delivering computational solutions for complex design goals in the fields of architecture, design, and engineering. The workshop was based on Data-Driven Strip Morphologies and Computational Pen Plotting.

A Guest Lecture from Valentina Sumini on the invention of new computational design methods for multi-performance habitats

This followed by a Guest Lecture from Valentina Sumini, Ph.D., is Space Architect and Research Affiliate at MIT Media Lab in Responsive Environments and Space Exploration Initiative, and Visiting Professor at Politecnico di Milano, where she teaches the course “Architecture for Human Space Exploration” at the School of Architecture, Urban Planning, and Construction Engineering. The presentation included comprehensive know-how on the invention of new computational design methods for multi-performance habitats. A vision of making humans a multi-planetary species with the help of computational design helped in understanding the dynamic facets of its adaptability.

Joseph Sarafian presenting his studio works that integrate fabrication as well as design development in complex form-making.

An eccentric presentation on the journey of Virtual Reality in architecture by Hamid Hassanzadeh, founder of PA.

The guest lecture was Followed by an eccentric presentation on the journey of Virtual Reality in architecture by Hamid Hassanzadeh, an Iranian architect, computational designer, researcher, speaker, and the founder of PA. Since the inception of PA in 2023, he has interviewed many architects and designers. Previously, he also conducted and supported workshops, organized events, and lectures. Globally, he has shared knowledge about computational and parametric design tools. In this session, Hassanzadeh created an engaging narrative to simplify the complex journey of parametric architecture and computational design right from inception to its current scenario.

This was followed by a vote of thanks by organizers & speakers, Hamid Hassanzadeh (Turkey/Iran) – Founder & Editor in Chief, ParametricArchitecture) and Sushant Verma (India) – Co-founder & Design Head, rat[LAB] Studio.

Entic: Shaping A Sustainable, Efficient And Smart Future With Next

A company which helps enterprises improve operating models, create more efficient buildings and deliver superior data along with customer and investor value is Entic helps building owners and operators achieve impactful utility cost reduction and increased asset value through greater operational visibility, under an affordable, easy-to-implement subscription model. The company’s proven solution generates specific actions for building operators to take, lowering their operational costs and achieving peak energy performance. Entic provides IoT cloud-based technology, enabling business outcomes around operational value, sustainability goals, and increased asset value. The company’s proven solution captures real-time building data and offers continuous diagnostics leading to the delivery of specific actions for internal facility teams. The reduction in electricity costs is often achieved through low-cost and no-cost recommendations, called prescriptions. Entic’s building analytics platform has demonstrated on an average 8-12% energy reduction across facilities and portfolios, translating into meaningful value creation. The Entic solution has been deployed by some of the most recognized names in real estate, hospitality, healthcare and sports venues. Entic’s technology can be found in iconic properties including Miami’s Marlins Park, the Willis Tower in Chicago, and the Park Avenue Tower in New York City.

Entic’s Inception

The name “Entic” is derived from the words ‘energy’ and ‘analytics’. Its logo features the Greek letter eta in lowercase, emulating the “n” in its name. The eta symbol is used in various branches of science and engineering to represent efficiency.

Today’s Leadership

Zach Posner, President and CEO, has served on Entic’s board of directors since 2023 and brings his extensive expertise in scaling enterprise software businesses to the company. A successful entrepreneur, Posner has also served as an adjunct professor at USC and has worked in venture capital prior to joining Entic. An Optimal Mix of Technology and Human Intelligence Entic focuses on portfolios, with the right technology mix, human intelligence and the ability to scale. It helps organizations to adopt proven tools and practices to drive significant efficiency and reduce operating expenses. Portfolios • The biggest opportunity for financial savings and sustainability impact. • The company has proven playbook, field-tested by Hilton, Blackstone and other enterprise customers. Technology Mix • The right mix of software (analytics), hardware (IoT smart sensors), and expertise in energy efficiency. • Remote Energy Center delivers timely recommendations called prescriptions, to resolve low-cost or no-cost fixes to building equipment issues. Human Intelligence • Entic’s Remote Energy Monitoring Center keeps eyes on the buildings and is staffed by experienced engineers and HVAC system specialists. The company’s technical support staff can act as an extension to the customers’ facilities management teams to resolve complex issues as they arise. • Entic’s Customer Success team onboards new customers and provides ongoing reporting and quarterly business reviews. Ability to Scale • Making the data relevant to people so they can make the decisions and take specific actions. • The company’s analytics platform captures real-time data from many sensors: temperature, gas, electric, kW and can detect symptoms with this continuous diagnostic process. Once the data is analyzed, the team can spot the issues and deliver specific recommendations, prescriptions, so that the on-site building operators can take action. • Entic empower building owners and operators with the tools to see how their systems are performing, how to measure the efficiency and let them know when systems are not performing as they should. • With a list of recommendations, Entic shows customers how to fix the issues and what it is costing, so customers can prioritize items and act to combat drift.

Delivering Cutting-Edge Innovation in Energy and Utilities

Entic works to consistently evolve its technology platform, with the help of programs that integrate and respond to customer recommendations. The company has a team of highly specialized skills and deep domain experts in energy efficiency, HVAC systems, software development, and engineering. The company has witnessed a proven track-record, demonstrating results with enterprise customers and industry leaders. Entic has partnered three strategic investors:

Achieving Business Excellence and Global Recognition

Entic has been successful in delivering solutions across a range of facilities and portfolios. Regardless of size, building-function or geography, the company has been able to identify waste and suggestions for taking corrective action, translating into enhanced value creation and a quick ROI (within in a year in most cases). Blackstone, the world’s largest private commercial real estate owner, began exploring energy saving technologies offered by Entic to reduce its costs and energy footprint. The private equity firm vetted approximately 20 companies, and Entic was selected as “Best in Class” solution for portfolio-wide utility waste reduction. Don Anderson, Chief Sustainability Officer at Blackstone, said “Blackstone companies have deployed over $1 million in handheld diagnostic tools to improve mechanical system O&M. Entic takes this approach to the next level by providing visibility and remote diagnostics associated with BAS deployment. 15% enterprise-wide energy cost savings are common as HVAC systems are tuned and properly controlled. This makes our buildings more comfortable and cost competitive, but also drives portfolio-wide consistency across our building engineers and management companies.” Entic has documented energy savings for enterprise customers in the hospitality sector as well. Hilton Worldwide has witnessed a 10 percent utility reduction across the portfolio of properties where it has deployed Entic’s analytics platform. The Entic platform has helped Hilton consistently witness energy, carbon and cost savings across their portfolio, supporting

Great Success is Built on Challenges

Gratefully, Entic has seen significant growth, traction and received strong strategic investments to support its expansion. “As witnessed in many other industries, there is an inherent challenge to move technology into a new space, for the first time. We overcome this challenge, by truly partnering with our customers to change the way that they operate their buildings, and make sense of an extraordinary amount of data to provide actionable insights and achieve business outcomes,” said Zach.

A Road Map to Enhanced Analytical Capabilities

Best International Trading Brokers In 2023

Investing in US stocks can be difficult, especially if you’re outside of the US.

As there is a myriad of international brokers online, choosing the right broker is challenging.

To find the right broker, you need to watch out for several factors.

Firstly, you need to consider the commissions/fees of the broker.

If you’re using a broker that has commissions, your profits will be slashed.

Secondly, you need to check the minimum deposit amount.

If you’re a beginner with limited funds, you need to use a broker that has little to no minimum deposit.

In addition, the broker needs to have exceptional customer service.

This includes the time to connect to a call or a live chat, and how professional the customer support representatives are.

Considering the above factors, this article consists of the top 3 best international trading brokers in 2023.

Best International Trading Brokers

The best international trading brokers are Interactive Brokers, TD Ameritrade, and TradeStation.

These brokers have little to no fees, a wide range of investment selections, and provide great customer service.

If you’re looking to invest in US stocks like Apple, Microsoft, or Disney, you need to use an international broker.

If you’re using a local exchange, you may not have access to global markets.

One of the most important factors when considering an international broker is their commissions/fees.

Hence, every broker in this article has little to no stock and ETF commissions so you can maximize your profits.

Here are the best international trading brokers:

1. Interactive Brokers

Rating: 4.5/5

⭐⭐⭐⭐⭐

Interactive Brokers is the largest trading platform in the US by daily average revenue trades.

They have very low commissions/fees, at $0.005 per share (for US stocks), and global access to stocks, options, bonds, currencies, funds, and futures.

You can invest in 135 markets, 33 countries, in 23 currencies.

In addition, you can use Interactive Brokers in 334 countries including Singapore, Germany, Australia, and others.

For client accounts, there is a $0 account minimum.

However, the only setback is that there is an inactivity fee for IBKR PRO accounts that do not meet a net liquidation value of $100,000.

For IBKR PRO accounts, you need to pay a monthly maintenance fee of $10 USD if you do not meet a net liquidation value of $100,000 and $10 USD in commissions incurred in a month.

If you’re using IBKR LITE, there is no inactivity fee.

Since 2005, Interactive Brokers has received numerous awards for the best online broker by various publications.

Barron’s, one of the leading financial news sites in the US, has given Interactive Brokers the highest ratings for eleven consecutive years, from 4.5 stars to 5 stars.

This including a range of factors such as user experience, customer service, security, and costs.

If you have an issue on the trading platform, there are multiple ways that you can use to contact Interactive Brokers including email, phone, and live chat.

Whether you’re a professional or a beginner investor, Interactive Brokers is a great choice for international investing.

Pros

One of the smartest order router

More than 4,300 zero transaction fee mutual funds

A myriad of investment selections

Cons

You cannot use the smart order router if you’re an IBKR Lite customer

The website has a complicated user interface

There’s an inactivity fee of $20 per month

2. TD Ameritrade

Rating: 4.0/5

⭐⭐⭐⭐

TD Ameritrade allows international investors to trade in the US market.

It offers $0 commissions on US stocks, ETF, and options trades.

This puts TD Ameritrade above Interactive Brokers in commissions.

In addition, there are no deposit minimums, trading minimums, or hidden fees.

TD Ameritrade has received multiple awards from chúng tôi NerdWallet, and Investor’s Business Daily for its commissions, research tools, as well as ease of use.

The online broker offers a ton of educational resources including market news, articles, and webcasts.

In addition, they offer free online courses that are easy to follow.

However, TD Ameritrade does not have fractional shares.

Another setback is that getting your account approved and contacting customer support can take a long time.

That said, you can usually find an answer to your issue by browsing through their educational resources in multiple formats (videos, articles, quizzes, etc.)

All in all, TD Ameritrade is great for beginners because of its extensive educational resources—though it can be overwhelming for some people.

Pros

Zero commission stock, options, and ETF trades

Great for beginners and education on investing

No account minimum

Cons

You may have to use more than one trading system

There are no fractional shares

Multiple platform outages in 2023

3. TradeStation

Rating: 4.0/5

⭐⭐⭐⭐

TradeStation provides modern trading technology and online brokerage services to American and international traders.

It has market monitoring, analysis, and charting tools that help you to easily identify trading opportunities.

In addition, TradeStation offers $0 commission stocks, options, and futures.

Options go as low as $0.50 per contract, while futures go as low as $0.85 per contract per side.

TradeStation’s trade execution speed is almost instant, and they have almost 100% uptime.

Their fills are instantaneous and accurate, unlike other brokers where delays are common.

This makes them a reliable broker for active investors.

A setback about TradeStation is that they do not provide DRIP investing, while other brokers do.

DRIP is a dividend reinvestment program that allows you reinvest your dividends for fractional shares.

This negatively impacts long-term investors.

One of the most underrated features on TradeStation is the ability to make an order to sell your contract when the underlying security meets your price target.

Most brokers do not have this feature.

If you’re looking for accuracy, fast trade execution, and highly customizable charting too, TradeStation is perfect for you.

Pros

Zero commission stock, options, and ETF trades

Stable platform with a 99% uptime

Great charting and technical analysis tools

Cons

How can I buy US stocks internationally?

You can buy US stocks internationally by using a broker that has access to the US market like Interactive Brokers or TD Ameritrade.

However, you need to convert your local currency into USD first before you can buy US stocks.

Not every brokerage has access to the US market.

Hence, before you create an account with a brokerage, make sure to check whether they have access to the US market first.

What is the best stock trading website for beginners?

The best stock trading website for beginners is TD Ameritrade.

TD Ameritrade offers a ton of educational resources including market news, articles, and webcasts that beginners will benefit from.

Each resource is searchable and filterable by topic.

An alternative stock trading website for beginners is Fidelity, which also provides extensive research and education.

Conclusion

Before you choose a brokerage firm, there are many factors that you need to consider.

Some of the factors include your capital, trading style, and transaction frequency.

Furthermore, the broker needs to have reasonable commissions/fees, ease of use, customer service, and more.

This will allow you to shortlist the best brokers based on your needs.

In comparison with the three brokers, the best overall is Interactive Brokers (for international trading).

It has a minimum deposit of $0, which is great for people who have limited funds.

In addition, it has a low fee of $0.005 per share for the pro platform or 1% of trade value and $0 for IBKR Lite.