My mother was always the last parent to pick me up from gymnastics practice. While other moms arrived in jeans, shed sweep in wearing a power suit, fresh from her role as a senior marketing executive at a major software company. At the time, it was a bit embarrassing. Looking back, I realize I was witnessing someone who refused to accept artificial limitations on what she could achieve.Years later, as a CMO, Ive come to appreciate how those early lessons shaped my understanding of professional possibilities. As a CMO in the 80s, my mother was a trailblazerit was not typical for a woman to have a seat at the board table. But Ive also learned that even with strong role models, we can still construct invisible barriers that limit our potential.These self-imposed ceilings manifest in unexpected waysnot just in career aspirations, but in how we think about work itself. Years before remote work became mainstream, I questioned another artificial boundary: the assumption that effective leadership required a physical office. The answers about where and how we could work seemed predetermined by longstanding corporate norms, until I proved otherwise.Wheres your artificial ceiling?The pattern of self-limitation is pervasive in the business world, especially in how we perceive career progression. I have personally experienced how these artificial barriers affect leaders, restricting our potential for further growth and advancement despite our knowledge of customers, market dynamics, and business strategy. Nevertheless, numerous skilled marketing leaders, including myself until recently, hesitate to pursue a trajectory beyond CMO. This is not due to a lack of capability, but rather because we have internalized certain assumptions about our career path direction and the roles that align with our expertise.The same can be said for other professions. Regardless of your department or title, where do you see yourself topping out? Whats the limit? And why is that the limit? Ask yourself those questions. And then make sure the ceiling you envision genuinely where you want your ceiling to be. (Of course, not everyone aspires to be a CEO; Im talking about aligning your perceived ceiling with your desired ceiling.)Break through the ceilingMy own process of breaking through these limitations began with redefining success on my terms. That meant moving beyond traditional career metrics to focus on creating lasting impact. To me, this meant developing the next generation of diverse business professionals, building high-performance teams rooted in different perspectives, and pursuing roles challenging conventional wisdom about career progression.Breaking through artificial ceilings is about more than career paths. Its about how we work. Long before the recent global shift to remote work, I chose to lead my teams from a distance. This was in an era when many questioned whether remote leadership could truly work. But Ive built and led high-performing teams across distances for years, proving that physical presence doesnt define leadership impact. Today, my long-term success as a remote executive serves as evidence that meaningful mentorship, team development, and career growth dont require shared office space.My professional goals have evolved beyond the CMO rolea goal that once seemed beyond my scope but now forms the core of my professional vision. The interesting thing is that breaking through the limitations was never just about moving up the ladder; its more that I realized that the metrics that matter to me, and the impact I want to have are beyond the CMO role. My perceived ceiling now aligns with my desired ceiling.Elevate others along the wayThe process of dismantling these self-imposed barriers isnt just personal; its about creating ripple effects throughout organizations as well as our families, social circles, communities, and more. In my role mentoring emerging business professionals, Ive seen how one person breaking through their perceived limitations can inspire others to do the same. (Really!)Its so much easier to recognize and dismantle artificial ceilings when youre doing so in an environment that actively encourages it. Again, this doesnt require physical proximity. When we challenge assumptions about where and how work gets done, we open new possibilities for talent, collaboration, and achievement. My teams have consistently demonstrated that leadership excellence transcends physical location. Thats why, for your sake and for the sake of the people around you, I strongly encourage you to take the lead in creating that environmentwherever you are. Empowering employees to challenge self-imposed boundaries requires intentional action:Actively questioning assumptions about traditional career trajectoriesBuilding support systems that encourage ambitious professional movesDeveloping teams that celebrate diverse perspectives and approachesCombining specialist expertise with broader leadership skillsCreating opportunities for others to expand their perceived limitationsCan you imagine the collective power of everyone redefining success, questioning assumptions, dismantling boundaries, and striving for their full potential? The potential impact on their individual success and career satisfaction is pretty amazing. Plus, you can combine that with the impact on the overall organization as people become more intentional about excellence and achievement.Even if youre not in a position to spearhead a cultural change within your own organization, you can still lead by example. Create a new pathway for others to follow.Turn uncertainties into possibilitiesFor those questioning their own artificial ceilings, start by examining your automatic responses to career opportunities. Do you immediately rule out certain roles? Do you assume some positions are out of reach? Challenge these assumptions. Consider whether youre limiting yourself based on outdated notions of whats possible.Its okay to be uncertain; thats natural when youre in a new scenario and pushing through barriers. But that means youre getting somewhere. It means youre turning uncertainties into possibilities.I started my career stuffing envelopes. To get to the C-suite, there had to be a lot of new scenarios, and a lot of barriers to push through. And Im still working on it. The key is recognizing these self-imposed barriers for what they are: artificial constructs that can be dismantled with intention and support.The next time you encounter an opportunity that seems just beyond your reach, pause and ask yourself: Is this a real ceiling, or one Ive built myself? The answer might reveal possibilities you never considered achievableand the first step toward breaking through to your full potential.Melissa Puls is chief marketing officer and SVP of customer success at Ivanti.

The complexion of this fast-paced world has dramatically changed with the assistance of smartphones which bring us closer to each other digitally. Somewhere though, we have lost the magic of silence and the privilege of enjoying moments that are now lost in the symphony of notifications and buzz of social media apps.Now is the time for most of us to give serious thought to the idea of a digital detox regime to step back and reclaim our Me Time. Minimalist phones, detox apps, and of course your self-restraint do help from getting lost down the rabbit hole of dooms scrolling. Those measures however only so far, until the moment you cannot resist the lure of the beautiful smartphone screen. The Back to Reality phone-locking box is a gadget that wants to curb your phone addiction once and for all.Designer: Alessandro PenneseCompared to other digital detox gadgets where the ultimate control to break the shackles lies with the user, this device takes that privilege away. Theres no way to get hold of your phone once the lid is closed and you set the time you will go for a digital detox routine even if that means going cold turkey for hours at end. You can only take important calls via the interface on top, but cannot make any exceptions for allowed apps. The only time you can open and access your smartphone is when the timer countdowns to zero after the designated time. This comes in handy when you are working on deadlines, in the middle of a family reunion, or meditating.Eventually, you get used to this regime and down the line, your screen time decreases. Its like training your brain to stay away from the screen and going for long-term dopamine release rather than rejoicing the silly short-term dopamine bursts that eventually trigger brain rot. The device can also be useful to regulate your kids screen time as well, to trigger better sleeping habits and avoid the overuse of phones. Some would argue that the gadget is a gimmick and wont be able to solve the inherent purpose but its the exact opposite. Since therell be a price tag on the device, users will subconsciously have the urge to use it regularly. In a way, it is better than minimalist phones that force things on you rather than work around them and regulate usage!The post This digital detox gadget locks your phone away to reclaim precious moments first appeared on Yanko Design.

In times of disaster, every second counts. However, for individuals with myopia, the absence of corrective lenses can significantly hinder their ability to navigate and make life-saving decisions. Recognizing this challenge, Three Days to See introduces an innovative solution: emergency glasses designed to provide immediate and adjustable vision correction in critical moments.Designer: Song Zetong, Deng Chenxi, Fan Yichen, Luo Yutong, and Du ChenChenMyopia, commonly known as nearsightedness, is a vision condition where distant objects appear blurry due to light focusing in front of the retina rather than directly on it. This condition affects millions worldwide, and those who rely on prescription glasses face an additional layer of vulnerability in emergencies. Whether fleeing a natural disaster, evacuating from a fire, or responding to an accident, the ability to see clearly is essential for survival.Stress and panic are natural responses to emergency situations. Under such pressure, individuals often forget essential items, including their glasses. Without proper vision, tasks as simple as reading signs, following escape routes, or identifying dangers become nearly impossible. This can lead to an increased risk of injury, delayed response times, and heightened anxiety. Another common issue is that many people have loose-fitting glasses, which can easily fall off during evacuation, further complicating their ability to navigate and respond to emergencies. Three Days to See addresses this issue by offering a practical and accessible alternative for those who need immediate vision correction.The key innovation behind Three Days to See is its ability to provide rapid and customizable vision correction without the need for prescription lenses. These emergency glasses use soft liquid lenses filled with highly refractive silicone oil. Users can adjust the diopter in five increments by pressing fluid-filled sacs integrated into the frame. This simple mechanism allows individuals with varying degrees of myopia to quickly obtain clear vision without the need for traditional glasses or professional adjustments.Additionally, these glasses feature a strong adhesive at the center of the top frame, which sticks to the forehead, securing the glasses in place without relying on traditional side arms. This design eliminates issues of improper fit caused by varying head sizes and prevents the glasses from slipping off during sudden movements or evacuations. By resting on the nose and being anchored by the forehead adhesive, the glasses provide a stable and comfortable fit for different users.Designed to be included in emergency kits, these glasses serve as a crucial survival tool. Their lightweight and durable construction ensures they can be stored for extended periods without deterioration. Whether used in a home emergency preparedness kit, workplace safety gear, or humanitarian aid packages, these glasses provide a much-needed solution for myopic individuals facing unpredictable situations.Beyond the physical advantage of restored vision, Three Days to See also addresses the psychological impact of emergencies. Vision impairment in high-stress scenarios can lead to disorientation, frustration, and a sense of helplessness. By equipping individuals with the ability to see clearly, these glasses help maintain confidence, reduce anxiety, and improve decision-making under pressure.From a broader perspective, accessible emergency vision correction aligns with the growing focus on inclusive disaster preparedness. By considering the needs of individuals with myopia, Three Days to See contributes to a more comprehensive approach to emergency response, ensuring that no one is left at a disadvantage when it matters most.The post Stuck without Spectacles in an Emergency? These Genius Adjustable Lenses Could Save Your Life first appeared on Yanko Design.

Google is among the first companies to change the Gulf of Mexico to Gulf of America on its maps. Other sources for online maps have not yet followed Donald Trump's executive order.

Apple has made it possible for users to move their purchased content, such as apps and music, from one Apple Account to a different one of their choosing. Here's how it works.Purchases can now be migrated from one Apple Account to anotherThe change was revealed on Tuesday through a support document on the Apple website, which details the exact procedures and requirements for transferring purchases between Apple Accounts. Though the iPhone maker already lets users share purchases via Family Sharing, where all family members have access to apps, books, and movies that individual family members buy, Apple's latest purchase migration feature has an entirely different purpose.According to the company's support document, the purchase transfer feature is intended for users who use two Apple Accounts for different features on their device a primary account for iCloud and its associated functionality, and a secondary one for the App Store and media purchases. Continue Reading on AppleInsider | Discuss on our Forums

Apple has been researching how to work a display into the headband of the Apple Vision Pro, maybe to save users having to put it on to see if updates are done or perhaps give info to outside observers.A display could be added into the fabric of an Apple Vision Pro headbandThe headband on an Apple Vision Pro is of course used to keep the device on a user's head, but Apple has long been rumored to want more from it, such as charging. Now a newly-granted patent shows that Apple has worked extensively on adding a display to the headband, to benefit both the wearer and people around them."Notifications in headbands" also considers how a display could personalize an Apple Vision Pro. You know, for those times dozens of Apple Vision Pro users get together in person. Continue Reading on AppleInsider | Discuss on our Forums

Anthropic CEO Dario Amodei warns AI will reach genius-level capabilities by 2026, calling Paris Summit a "missed opportunity" as U.S. and European leaders clash over regulation of rapidly advancing artificial intelligence systems.Read More

Federal Communications Chair Brendan Carr has asked his agency to investigate Comcasts Diversity, Equity, and Inclusion (DEI) practices, reports Newsmax. We have received an inquiry from the Federal Communications Commission and will be cooperating with the FCC to answer their questions, Comcast spokesperson Joelle Terry confirms to The Verge.According to Newsmax, Carr said that FCC is looking for signs that the companys initiatives have violated federal employment law, writing: I expect that this investigation into Comcast and its NBCUniversal operations will aid the commissions broader efforts to root out invidious forms of DEI discrimination across all of the sectors the FCC regulates. Since taking control of the FCC last month, Carr has threatened to pull broadcast licenses of companies like Disney and CBS for airing content thats not friendly to Trump and conservatives. He has also ordered investigations into NPR and PBS for airing commercials, which fellow Commissioner Anna Gomez told The Verge was a Trump administration effort to weaponize the power of the FCC. Carr was a Trump appointee, and he wrote the Project 2025 chapter on how the FCC should rein in big companies.In addition to its cable, wireless, and internet services, Comcast owns a swath of broadcasters, including NBC Universal, streaming service Peacock, and many others. (Disclosure:Comcast is also an investor in Vox Media, The Verges parent company.)Under the new Trump administration, many companies are proactively winding down their DEI programs seemingly to avoid becoming targets. By the time an executive order on January 20th declared DEI was a corrupting force creating a divisive and dangerous preferential hierarchy, Meta had already disbanded its diversity team and Amazon had wound down some DEI programs; Google joined them less than a week ago.

Yann LeCun, Chief AI Scientist at Meta and one of the pioneers of modern AI, recently argued that autoregressive Large Language Models (LLMs) are fundamentally flawed. According to him, the probability of generating a correct response decreases exponentially with each token, making them impractical for long-form, reliable AI interactions.While I deeply respect LeCuns work and approach to AI development and resonate with many of his insights, I believe this particular claim overlooks some key aspects of how LLMs function in practice. In this post, Ill explain why autoregressive models are not inherently divergent and doomed, and how techniques like Chain-of-Thought (CoT) and Attentive Reasoning Queries (ARQs)a method weve developed to achieve high-accuracy customer interactions with Parlanteffectively prove otherwise.What is Autoregression?At its core, an LLM is a probabilistic model trained to generate text one token at a time. Given an input context, the model predicts the most likely next token, feeds it back into the original sequence, and repeats the process iteratively until a stop condition is met. This allows the model to generate anything from short responses to entire articles.For a deeper dive into autoregression, check out our recent technical blog post.Do Generation Errors Compound Exponentially?LeCuns argument can be unpacked as follows:Define C as the set of all possible completions of length N.Define A C as the subset of acceptable completions, where U = C A represents the unacceptable ones.Let Ci[K] be an in-progress completion of length K, which at K is still acceptable (Ci[N] A may still ultimately apply).Assume a constant E as the error probability of generating the next token, such that it pushes Ci into U.The probability of generating the remaining tokens while keeping Ci in A is then (1 E)^(N K).This leads to LeCuns conclusion that for sufficiently long responses, the likelihood of maintaining coherence exponentially approaches zero, suggesting that autoregressive LLMs are inherently flawed.But heres the problem: E is not constant.To put it simply, LeCuns argument assumes that the probability of making a mistake in each new token is independent. However, LLMs dont work that way.As an analogy to what allows LLMs to overcome this problem, imagine youre telling a story: if you make a mistake in one sentence, you can still correct it in the next one to keep the narrative coherent. The same applies to LLMs, especially when techniques like Chain-of-Thought (CoT) prompting guide them toward better reasoning by helping them reassess their own outputs along the way.Why This Assumption is FlawedLLMs exhibit self-correction properties that prevent them from spiraling into incoherence.Take Chain-of-Thought (CoT) prompting, which encourages the model to generate intermediate reasoning steps. CoT allows the model to consider multiple perspectives, improving its ability to converge to an acceptable answer. Similarly, Chain-of-Verification (CoV) and structured feedback mechanisms like ARQs guide the model in reinforcing valid outputs and discarding erroneous ones.A small mistake early on in the generation process doesnt necessarily doom the final answer. Figuratively speaking, an LLM can double-check its work, backtrack, and correct errors on the go.Attentive Reasoning Queries (ARQs) are a Game-ChangerAt Parlant, weve taken this principle further in our work on Attentive Reasoning Queries (a research paper describing our results is currently in the works, but the implementation pattern can be explored in our open-source codebase). ARQs introduce reasoning blueprints that help the model maintain coherence throughout long completions by dynamically refocusing attention on key instructions at strategic points in the completion process, continuously preventing LLMs from diverging into incoherence. Using them, weve been able to maintain a large test suite that exhibits close to 100% consistency in generating correct completions for complex tasks.This technique allows us to achieve much higher accuracy in AI-driven reasoning and instruction-following, which has been critical for us in enabling reliable and aligned customer-facing applications.Autoregressive Models Are Here to StayWe think autoregressive LLMs are far from doomed. While long-form coherence is a challenge, assuming an exponentially compounding error rate ignores key mechanisms that mitigate divergencefrom Chain-of-Thought reasoning to structured reasoning like ARQs.If youre interested in AI alignment and increasing the accuracy of chat agents using LLMs, feel free to explore Parlants open-source effort. Lets continue refining how LLMs generate and structure knowledge.Disclaimer: The views and opinions expressed in this guest article are those of the author and do not necessarily reflect the official policy or position of Marktechpost. The post Are Autoregressive LLMs Really Doomed? A Commentary on Yann LeCuns Recent Keynote at AI Action Summit appeared first on MarkTechPost.

In this tutorial, we will build an advanced AI-powered research agent that can write essays on given topics. This agent follows a structured workflow:Planning: Generates an outline for the essay.Research: Retrieves relevant documents using Tavily.Writing: Uses the research to generate the first draft.Reflection: Critiques the draft for improvements.Iterative Refinement: Conducts further research based on critique and revises the essay.The agent will iterate through the reflection and revision process until a set number of improvements are made. Lets dive into the implementation.Setting Up the EnvironmentWe start by setting up environment variables, installing required libraries and importing the necessary libraries:pip install langgraph==0.2.53 langgraph-checkpoint==2.0.6 langgraph-sdk==0.1.36 langchain-groq langchain-community langgraph-checkpoint-sqlite==2.0.1 tavily-pythonimport osos.environ['TAVILY_API_KEY'] = "your_tavily_key"os.environ['GROQ_API_KEY'] = "your_groq_key"from langgraph.graph import StateGraph, ENDfrom typing import TypedDict, Listfrom langchain_core.messages import SystemMessage, HumanMessagefrom langgraph.checkpoint.sqlite import SqliteSaverimport sqlite3sqlite_conn = sqlite3.connect("checkpoints.sqlite",check_same_thread=False)memory = SqliteSaver(sqlite_conn)Defining the Agent StateThe agent maintains state information, including:Task: The topic of the essayPlan: The generated plan or outline of the essayDraft: The draft latest draft of the essayCritique: The critique and recommendations generated for the draft in the reflection state.Content: The research content extracted from the search results of the TavilyRevision Number: Count of number of revisions happened till nowclass AgentState(TypedDict): task: str plan: str draft: str critique: str content: List[str] revision_number: int max_revisions: intInitializing the Language ModelWe use the free Llama model API provided by Groq to generate plans, drafts, critiques, and research queries.from langchain_groq import ChatGroqmodel = ChatGroq(model="Llama-3.3-70b-Specdec")Defining the PromptsWe define system prompts for each phase of the agents workflow (you can play around with these if you want):PLAN_PROMPT = """You are an expert writer tasked with creating an outline for an essay.Generate a structured outline with key sections and relevant notes."""WRITER_PROMPT = """You are an AI essay writer. Write a well-structured essay based on the given research.Ensure clarity, coherence, and proper argumentation.------{content}"""REFLECTION_PROMPT = """You are a teacher reviewing an essay draft.Provide detailed critique and suggestions for improvement."""RESEARCH_PLAN_PROMPT = """You are an AI researcher tasked with finding supporting information for an essay topic.Generate up to 3 relevant search queries."""RESEARCH_CRITIQUE_PROMPT = """You are an AI researcher refining an essay based on critique.Generate up to 3 search queries to address identified weaknesses."""Structuring Research QueriesWe use Pydantic to define the structure of research queries. Pydantic allows us to define the structure of the output of the LLM.from pydantic import BaseModelclass Queries(BaseModel): queries: List[str]Integrating Tavily for ResearchAs previously, we will use Tavily to fetch relevant documents for research-based essay writing.from tavily import TavilyClientimport ostavily = TavilyClient(api_key=os.environ["TAVILY_API_KEY"])Implementing the AI Agents1. Planning NodeGenerates an essay outline based on the provided topic.def plan_node(state: AgentState): messages = [ SystemMessage(content=PLAN_PROMPT), HumanMessage(content=state['task']) ] response = model.invoke(messages) return {"plan": response.content}2. Research Plan NodeGenerates search queries and retrieves relevant documents.def research_plan_node(state: AgentState): queries = model.with_structured_output(Queries).invoke([ SystemMessage(content=RESEARCH_PLAN_PROMPT), HumanMessage(content=state['task']) ]) content = state['content'] if 'content' in state else [] for q in queries.queries: response = tavily.search(query=q, max_results=2) for r in response['results']: content.append(r['content']) return {"content": content}3. Writing NodeUses research content to generate the first essay draft.def generation_node(state: AgentState): content = "\n\n".join(state['content'] or []) user_message = HumanMessage(content=f"{state['task']}\n\nHere is my plan:\n\n{state['plan']}") messages = [ SystemMessage(content=WRITER_PROMPT.format(content=content)), user_message ] response = model.invoke(messages) return {"draft": response.content, "revision_number": state.get("revision_number", 1) + 1}4. Reflection NodeGenerates a critique of the current draft.def reflection_node(state: AgentState): messages = [ SystemMessage(content=REFLECTION_PROMPT), HumanMessage(content=state['draft']) ] response = model.invoke(messages) return {"critique": response.content}5. Research Critique NodeGenerates additional research queries based on critique.def research_critique_node(state: AgentState): queries = model.with_structured_output(Queries).invoke([ SystemMessage(content=RESEARCH_CRITIQUE_PROMPT), HumanMessage(content=state['critique']) ]) content = state['content'] or [] for q in queries.queries: response = tavily.search(query=q, max_results=2) for r in response['results']: content.append(r['content']) return {"content": content}Defining the Iteration ConditionWe use the number of iterations as a condition to decide if we want to continue revising or end the loop. So the agent continues revising the essay until the maximum revisions are reached.def should_continue(state): if state["revision_number"] > state["max_revisions"]: return END return "reflect"Building the WorkflowWe define a state graph to connect different nodes in the workflow.builder = StateGraph(AgentState)builder.add_node("planner", plan_node)builder.add_node("generate", generation_node)builder.add_node("reflect", reflection_node)builder.add_node("research_plan", research_plan_node)builder.add_node("research_critique", research_critique_node)builder.set_entry_point("planner")builder.add_conditional_edges("generate", should_continue, {END: END, "reflect": "reflect"})builder.add_edge("planner", "research_plan")builder.add_edge("research_plan", "generate")builder.add_edge("reflect", "research_critique")builder.add_edge("research_critique", "generate")graph = builder.compile(checkpointer=memory)We can also visualize the graph using:#from IPython.display import Image#Image(graph.get_graph().draw_mermaid_png())Running the AI Essay Writerthread = {"configurable": {"thread_id": "1"}}for s in graph.stream({ 'task': "What is the difference between LangChain and LangSmith", "max_revisions": 2, "revision_number": 1,}, thread): print(s)And we are done, now go ahead and test it out with different queries and play around with it. In this tutorial we covered the entire process of creating an AI-powered research and writing agent. You can now experiment with different prompts, research sources, and optimization strategies to enhance performance. Here are some future improvements you can try:Build a GUI for better visualization of working of agentImprove the end condition from revising fix number of times to end when you are satisfied by the output (i.e, including another llm node to decide or putting human in the loop)Add support to write directly to pdfsReferences:(DeepLearning.ai)https://learn.deeplearning.ai/courses/ai-agents-in-langgraph Vineet KumarVineet Kumar is a consulting intern at MarktechPost. He is currently pursuing his BS from the Indian Institute of Technology(IIT), Kanpur. He is a Machine Learning enthusiast. He is passionate about research and the latest advancements in Deep Learning, Computer Vision, and related fields.Vineet Kumarhttps://www.marktechpost.com/author/vineet1897/Efficient Alignment of Large Language Models Using Token-Level Reward Guidance with GenARMVineet Kumarhttps://www.marktechpost.com/author/vineet1897/Chain-of-Associated-Thoughts (CoAT): An AI Framework to Enhance LLM ReasoningVineet Kumarhttps://www.marktechpost.com/author/vineet1897/Creating an AI Agent-Based System with LangGraph: Putting a Human in the LoopVineet Kumarhttps://www.marktechpost.com/author/vineet1897/Creating an AI Agent-Based System with LangGraph: Adding Persistence and Streaming (Step by Step Guide) [Recommended] Join Our Telegram Channel