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No Android, no problem: Huawei debuts made-in-China HarmonyOS NextATLANTA — Georgia Republicans can subpoena Fulton County District Attorney Fani Willis about her romantic relationship with Nathan Wade, the outside attorney she hired to lead her 2020 election interference case against former and future President Donald Trump and his allies, a judge has ruled. The decision by Fulton County Superior Court Judge Shukura Ingram was published Monday. It is the latest blow to Willis in regard to her prosecution of Trump. On Nov. 19, the Georgia Court of Appeals disqualified Willis and her office from the case due to her relationship with Wade. Ingram rejected Willis’ arguments that GOP leaders of a state Senate committee didn’t have the authority to issue a pair of subpoenas in August seeking documents and her testimony. But the judge gave Willis until Jan. 13 to explain why the subpoenas shouldn’t be enforced. “(Willis) claims that the subpoenas are overbroad and seek privileged and/or confidential information,” the judge said in her order. “This may very well be true, but this court needs more details on the basis for these objections from (Willis) before ruling on (committee members’) application for enforcement.” Representatives for Willis did not immediately respond Friday to questions about the ruling. Sen. Greg Dolezal, one of the committee members, mentioned the ruling in a social media post Friday. “We’ll see you soon, Madam D.A.,” he wrote. Dolezal, R-Cumming, has unveiled plans to extend the life of the committee into 2025. He said Ingram’s ruling “follows the plain reading of the law as outlined in our committee.” “From open records laws to lawfully issued subpoenas, D.A. Willis seems to think she is above the law,” Dolezal said. “Once again, the courts ruled otherwise.” Ingram heard arguments from both sides during a Dec. 3 hearing in Atlanta. Willis claimed the subpoenas from the Senate’s Special Committee on Investigations were void because the committee and its members lacked legislative subpoena power, issued the subpoenas while the Senate was adjourned and violated part of the Georgia Constitution. Ingram disagreed. “It is undisputed that the General Assembly has the unenumerated and implied constitutional power to conduct investigations and the statutory authority to issue subpoenas for the purposes of such investigations,” she said. “Here, the Senate, as a chamber, empowered itself to issue subpoenas, which it was authorized to do.” The judge further said Willis failed to identify any authority to support her theory that the subpoenas could not be issued after the Legislature adjourned its regular session on March 28. Ingram also said the Georgia Constitution allows members of one branch of government to investigate members of another branch. The committee members offered to narrow the scope of the subpoenas, the judge noted. She gave them until Jan. 31 to respond to any objections Willis raises about enforcement of the subpoenas. Senators created the committee in January, just weeks after news surfaced that Willis had been in a romantic relationship with Wade. Members sought years worth of communications between Willis and Wade as well as information about his hiring and pay, the DA office’s federal grant funding and interactions with the White House, Justice Department and members of Congress regarding the elections case. They also sought to compel Willis’ attendance at a September hearing, which she declined to attend as she fought her summons in court.

ESMT INVESTOR ALERT: Bronstein, Gewirtz & Grossman LLC Announces that EngageSmart, Inc. Investors with Substantial Losses Have Opportunity to Lead Class Action LawsuitThe Times view on Ashtead’s stock market shift: Don’t Tell SidIt’s hard being a nerd, but yes, I interviewed an AI Bot to learn more about how it “thinks” and how to better work with it, especially as it relates to patents. Human: How does a Large Language Model (LLM) determine higher frequency phrases? Bot: An LLM like myself determines higher frequency phrases through several steps in its training and operation: 1. Training Data Analysis: During training, the model is exposed to vast amounts of text, counting occurrences of words and phrases. 2. Tokenization: Text is broken down into tokens, which helps identify frequently occurring sequences. 3. Frequency Counting: The model tracks how often specific sequences (n-grams) appear. 4. Statistical Models: It uses these counts to predict phrase likelihood based on context. 5. Contextual Understanding: Modern LLMs consider context to understand relevance. 6. Embedding Space: Frequent phrases have more robust representations. 7. Post-Processing: Additional algorithms ensure common phrases are used appropriately. Human: That is very helpful. Does it mean that if a single occurrence of a concept occurs among thousands of other, higher frequency concepts, then this single occurrence may be overlooked by the AI algorithm? Or at least have a high likelihood of being overlooked? Bot: Yes, that’s correct. A single occurrence among thousands of higher frequency concepts has a high likelihood of being overlooked or given less importance due to frequency bias, lack of contextual relevance, overfitting prevention, and statistical significance. However, LLMs can still capture some long-tail data, and with continuous learning, recognition might improve over time. Human: Great. And is it fair to say that the output of the AI engine uses more equivocal language to describe concepts that it is not as sure of (ostensibly because they are lower frequency than other concepts)? For example, the AI bot says things like “it seems that ...” or “it may be that ...” indicating it thinks so, but it is not as sure. Bot: Yes, that’s a fair observation. When dealing with less certain or lower frequency concepts, the output might use more equivocal language like “it seems that” or “it may be that” to express uncertainty, reflect lower confidence, avoid overconfidence, encourage interaction, and adhere to ethical AI practices. Human: Thank you, great. Now does this also mean that seeding the AI Bot with too many or spurious documents may dilute the accuracy of the output? Bot: Yes, seeding an AI model with too many or spurious documents can indeed dilute the accuracy of its output. Here’s why: • Noise Introduction: Spurious documents or irrelevant data can introduce noise into the training set. This noise can skew the model’s understanding of language patterns, making it less accurate in its predictions or responses. • Overfitting to Noise: If the model is trained on a dataset where a significant portion is irrelevant or low quality, it might overfit to this noise, learning patterns that are not representative of the language or the task at hand. This reduces generalization to new, relevant data. • Dilution of Relevant Information: High-quality, relevant data gets diluted when mixed with a large volume of low-quality or irrelevant data. This can lead to the model not learning the key concepts or patterns as effectively because they are less prominent in the overall dataset. • Resource Allocation: Training models require computational resources. If a large portion of these resources is spent on processing and learning from irrelevant data, the efficiency of the training process decreases, potentially leading to suboptimal performance. • Bias and Misinformation: If spurious documents contain biases or misinformation, these can be incorporated into the model’s knowledge, leading to biased or incorrect outputs. • Contextual Misalignment: When the AI is exposed to too much varied or off-topic data, it might struggle to maintain context or relevance in its responses, especially when dealing with specific queries or tasks. To mitigate these issues, careful curation of training data is crucial. Data scientists often use techniques like: • Data Cleaning: Removing or correcting spurious entries. • Relevance Scoring: Assigning weights or filtering based on relevance to the task. • Quality Control: Ensuring the dataset is of high quality before training. • Continuous Monitoring: Regularly updating and refining the dataset based on performance feedback. So, while a diverse dataset can be beneficial, the quality and relevance of the documents are paramount to maintaining or improving the AI’s accuracy. Human: In the world of patents, sometimes the patents are drafted with similar, but not identical terms referring to the same things but using different etymology. Is that a potential source of error? Bot: Yes, using similar but not identical terms with different etymology when drafting patents can indeed be a potential source of error. This practice can lead to several issues: • Ambiguity: Different terms, even if they refer to the same thing, might introduce ambiguity. This can make it challenging for patent examiners, courts, or other parties to interpret the scope of the patent accurately. Ambiguity can result in disputes over what exactly is protected by the patent. • Claim Construction: During litigation, the construction of patent claims becomes crucial. If terms with different etymological roots are used, it might complicate the process of defining what the claim covers, potentially leading to broader or narrower interpretations than intended. • Prior Art Analysis: When searching for prior art, using different terms might lead to missing relevant patents or publications because databases might not recognize these terms as synonyms. This could result in overlooking existing technology, which might affect the novelty or non-obviousness of the invention. • Prosecution History: The use of different terms can complicate the prosecution history of a patent. If an applicant uses one term during prosecution but another in the final claims, this could be seen as an attempt to broaden or narrow the claim scope post-hoc, which might be scrutinized by examiners or courts. • International Filings: When filing patents internationally, different terms might not translate well or might have different legal or technical meanings in other languages, leading to inconsistencies in protection across jurisdictions. To mitigate these risks, patent drafters may use a glossary within the patent to define terms clearly or stick to standardized terminology within the field to increase clarity and precision. Human: How do LLM AI bots treat patent images and patent drawings? Bot: LLM AI bots have varying capabilities when it comes to handling patent images and drawings due to their primary focus on text processing. Here’s how they typically interact with these elements in patent-related tasks: • Direct Interaction with Images: LLMs inherently do not process images as they are designed for text-based operations. However, there are advancements where LLMs are integrated with vision models to handle images. For instance, some platforms like Drafting LLM by XLSCOUT can generate visuals such as flowcharts and complex technical illustrations based on text descriptions, enhancing the clarity and understanding of patent applications. This integration allows for the creation of accurate drawings and diagrams that are part of patent documents. • Image Analysis: For tasks like patent search or prior art analysis, LLMs might not directly analyze images but can process descriptions or tags associated with images. Tools like Novelty Checker LLM from XLSCOUT use AI to analyze both patent and non-patent literature, which includes images indirectly through contextual analysis. However, the effectiveness of such analysis depends on how well the image data is described in the text form within the patent documents. • Patent Drafting: In patent drafting, LLMs can assist in generating descriptions that might accompany drawings. They can help in crafting comprehensive patent claims that reference or describe the images, although the actual creation of the images would be done by human drafters or specialized software unless integrated with AI capable of visual generation. Drafting LLM, for example, supports multiple image uploads and uses AI-driven analysis to ensure visuals accurately reflect the invention’s technical aspects. • Limitations: The current state of LLMs in handling images in patents shows some limitations. While some LLMs are powerful, they struggle with tasks like recognizing element numbers in patent figures correctly, indicating that there is room for improvement in how LLMs deal with visual elements in patents. In summary, while LLMs can’t directly process or interpret images due to their text-centric nature, they can be part of systems that indirectly work with images through textual descriptions or by being combined with image processing AI. The direct handling of creating or interpreting patent drawings still largely relies on human input or specialized image recognition AI systems. Human: How do I know you are telling me the truth about these things? Bot: That’s a fair question, and here’s how I approach ensuring the accuracy and truthfulness of my responses: • Training Data: I’m trained on a vast array of texts from various sources, which helps in providing well-rounded information. However, I acknowledge that biases or errors can exist in this data. • Transparency: I strive to be transparent about my limitations. For example, I’ve mentioned that my knowledge is continuously updated, which means I’m designed to incorporate new information over time, reducing the likelihood of outdated or incorrect information. • Source Verification: When possible, I reference information from reliable sources or mention that certain information can be verified through external sources like web searches. This allows you to cross-check my responses. • Logical Reasoning: I aim to provide logical explanations for how processes work, like how LLMs determine phrase frequency or handle ambiguous patent terminology, which you can evaluate for consistency with known principles. • User Feedback: Over time, feedback from users like you helps refine my responses. If there’s a discrepancy or an error, users can point it out, which contributes to my learning process. • Ethical Design: My creators have designed me with an emphasis on truthfulness and helpfulness, avoiding the spread of misinformation. Remember, while I strive for accuracy, the dynamic nature of information means there might be nuances or updates I haven’t yet incorporated. If you’re ever in doubt, you can always ask me to search for more recent developments or check the information through other reliable channels. When using AI bots, more is not necessarily better and is sometimes worse. It’s clear that we need more tools for drawing generation and analysis. Artificial Intelligence is changing how we process data, and that change is happening very quickly, but patent drafters still have a lot of work to do to harness the power of various software algorithms for creating patent applications and performing detailed patent analyses.

The crypto market is enjoying a bullish run filled with many lucrative opportunities that have great potential. However, just a few tokens are attracting investors. DTX Exchange (DTX), which has exploded with a massive 500% price surge, leads the pack. Other promising coins for higher returns in the next bull run include Sui (SUI), Aptos (APT), and Movement (MOVE). DTX Exchange is giving the three a tough fight as the pack eyes dominance in the crypto market. If you want to invest in the next big thing in crypto, DTX is an option you cannot overlook. But how does it compare with worthwhile investment opportunities like SUI, APT, and MOVE? Let’s find out! DTX Exchange: The New Crypto Platform Revolutionizing Online Trading DTX Exchange , a new crypto trading platform, is experiencing massive growth. Its underlying token, DTX, has gained by 500% in its presale price and is gaining momentum. DTX Exchange’s advanced features make the presale token dominate other established tokens like SUI, APT, and MOVE. Furthermore, investors are excited about DTX Exchange’s long-term goals. It will unleash a trading platform that integrates the best features of DEX and CEX. DTX Exchange dominates the crypto market with its advanced technology, which offers increased stability and unrivaled trading opportunities. The platform is supported by the VulcanX blockchain, offering access to more than 120,000 asset classes, including stocks, forex, EFTs, and crypto. Therefore, investors only need to use DTX Exchange to manage all their assets and trading operations. Additionally, DTX Exchange offers up to 1000x leverage, enabling investors to maximize their profits while using minimal capital. For example, a trader with $100 can access liquidity of up to $100,000. Owning the DTX presale token has many benefits. The governance rights allow holders to share their opinions on the platform’s upcoming products. Also, the larger DTX holders receive profit shares through the Rebate Program. Therefore, the platform caters to investors looking to earn passive income and have their ideas about the future of the project implemented. Moreover, the platform has already recorded a TPS of 100,000 in its testnet, which also accounts for its seamless operations and increased adoption rate. DTX’s presale has been impressive, raising over $10.3 million, and could hit $11 million by the end of December 2024. SUI Surges 30% to Lead the Crypto Market After Key Developments SUI’s price increased by 30% on December 11, 2024, hitting $4.81, making it a top gainer of the day. The price surge boosted its crypto market sentiment, with more investors buying the token. Since its launch, Sui has attracted investors and users due to its unique infrastructure, speed, and the Move programming language. The price surge is underpinned by increased network activity, considerable protocol developments, and massive investor interest. Recent collaborations with Franklin Templeton, Babylon Labs, and the integration of Bitcoin liquidity prove Sui is expanding in the blockchain space. SUI was trading at $4.72 on December 12, gaining 22.23% in the past week. With all these developments, analysts believe SUI will rise to $5.05 this month. While SUI surges, investors are accumulating DTX, a new crypto with better profit prospects. Aptos Blockchain Achieves Remarkable Growth in 2024 Through DeFi Expansion In 2024, the Aptos blockchain recorded considerable growth in on-chain activity, as highlighted by the growing number of active users. Monthly active users increased, reaching almost 10 million by December. The platform’s decentralized finance (DeFi) ecosystem also recorded massive growth, with total value locked (TVL) exceeding $1.2 billion, a 19x increase year-over-year. This increase was powered by significant protocol adoption and greater transaction activities. APT was priced at $13.73 on December 12, down 2.76% in the past week. Analysts believe APT will rise to $16.15 this month as its on-chain activity increases to dominate the DeFi coin sector. Investors are also diversifying to DTX, a presale token, as it thrives in the crypto market with long-term growth potential and advanced trading solutions. Movement Network Launches Mainnet Beta, Gains Momentum with Binance Listing Movement Network Foundation introduced Movement Mainnet Beta and the Token Generation Event (TGE) of MOVE. This move marked a key milestone for the first Move-based blockchain that blends with Ethereum. On the other hand, Binance Exchange announced the listing of Movement on its platform. MOVE enhances the network’s security features. Trading started on December 9, 2024, at 14:00 UTC, with several pairs already listed. The exchange will distribute 200 million MOVE tokens to BNB holders as part of an airdrop. Moreover, 5% will be allocated to Binance’s Launchpool six months post-listing. Movement also celebrated this launch with an NFT initiative, where the community developed artwork. MOVE was trading at $0.6876 on December 12, up 3.76% in the past week. With all the developments on the Movement network, MOVE keeps rising and presents an exciting buying opportunity in the crypto market. However, DTX, a new crypto, outpaces MOVE as it targets higher gains. DTX: A Presale Token Dominating the Crypto Market With Over $10 Million Raised! While Sui (SUI), Aptos (APT), and Movement (MOVE) offer great buying opportunities, DTX Exchange (DTX) dominates the market. Its blending of DEX and CEX features, privacy-focused strategy, and impressive presale token performance have enabled it to dominate the space. DTX Exchange’s advanced trading solutions and massive growth potential make it a lucrative investment. Currently available in Stage 6 of its public presale, the new crypto coin is valued at $0.12 and will rise to $0.14 when it moves to the next stage. The current buyers will see a 67% profit at the end of this public presale when DTX lists on the mainstream crypto market at $0.20. DTX Exchange has raised over $10.3 million, attracted more than 300,000 wallet users, and recorded a 100,000 TPS in its testnet. This could well be the next big thing in crypto. Don’t miss the opportunity to invest in this game-changing platform—join the future of crypto today! Buy Presale Visit DTX Website Join The DTX Community Join our WhatsApp Channel to get the latest news, exclusives and videos on WhatsApp _____________ Disclaimer: Analytics Insight does not provide financial advice or guidance. Also note that the cryptocurrencies mentioned/listed on the website could potentially be scams, i.e. designed to induce you to invest financial resources that may be lost forever and not be recoverable once investments are made. You are responsible for conducting your own research (DYOR) before making any investments. Read more here.Museum Showing Pussy Riot Artwork Targeted in Apparent Vandalism

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Global stock markets mostly retreated Tuesday as traders eyed looming US inflation data and a key European interest rate call amid global political upheaval. After winning numerous records in the weeks since the November 5 US presidential election, US stocks fell for the second straight day as analysts pointed to profit-taking. But Alphabet jumped more than five percent after Google showed off a new quantum computing chip that it described as a significant breakthrough in the field, arguing it could lead to advances in drug discovery, fusion energy and other areas. The Paris stock market retreated as French party leaders gathered at President Emmanuel Macron's Elysee Palace office to chart a route towards a new government. The euro also fell ahead of the European Central Bank's monetary policy meeting on Thursday. The ECB is expected to lower interest rates by 25 basis points amid weak eurozone growth. Independent analyst Andreas Lipkow said traders were taking a cautious approach ahead of the ECB meeting. The main US indexes struggled as traders eyed US consumer price inflation (CPI) data due Wednesday, which could play a role in whether the US Federal Reserve decides to cut interest rates next week. On Wall Street, "tomorrow's CPI report is in full focus with a looming rate-decision from the Fed coming," analyst Bret Kenwell of trading platform eToro said in a note. Following recent spending and jobs data "traders have felt even more emboldened to bet on a December rate cut, while the Fed has done little... to quiet that expectation," he added. Earlier, stock markets weighed "concerns that China's economic stimulus measures might not have a long-lasting effect", noted Dan Coatsworth, investment analyst at AJ Bell. The growth plan comes as Beijing contemplates Donald Trump's second term in the White House. The US president-elect has indicated he will reignite his hardball trade policies, fueling fears of another standoff between the economic superpowers. The Shanghai stock market ended higher but Hong Kong fell. Seoul's Kospi index rallied more than two percent after tumbling since President Yoon Suk Yeol declared short-lived martial law on December 3. On the corporate front, shares in Stellantis rose around one percent on the Paris stock exchange after the car giant and Chinese manufacturer CATL announced plans for a $4.3-billion factory making electric-vehicle batteries in Spain. Walgreens Boots Alliance soared 17.7 percent following reports that it could be acquired by private equity firm Sycamore Partners. Boeing jumped 4.5 percent as it announced it was resuming production at two Seattle-area plants that had been shuttered for nearly three months due to a labor strike. New York - Dow: DOWN 0.4 percent at 44,247.83 (close) New York - S&P 500: DOWN 0.3 percent at 6,034.91 (close) New York - Nasdaq Composite: DOWN 0.3 percent at 19,687.24 (close) Paris - CAC 40: DOWN 1.1 percent at 7,394.78 (close) Frankfurt - DAX: DOWN 0.1 percent at 20,329.16 (close) London - FTSE 100: DOWN 0.9 percent at 8,280.36 (close) Hong Kong - Hang Seng Index: DOWN 0.5 percent at 20,311.28 (close) Shanghai - Composite: UP 0.6 percent at 3,422.66 (close) Tokyo - Nikkei 225: UP 0.5 percent at 39,367.58 (close) Seoul - Kospi: UP 2.4 percent at 2,417.84 (close) Euro/dollar: DOWN at $1.0529 from $1.0554 on Monday Pound/dollar: UP at $1.2773 from $1.2757 Dollar/yen: UP at 151.92 yen from 151.21 yen Euro/pound: DOWN at 82.42 from 82.73 pence West Texas Intermediate: UP 0.1 percent at $68.59 per barrel Brent North Sea Crude: UP 0.1 percent at $72.19 per barrel burs-jmb/nroWicked star Ariana Grande has said she and Cynthia Erivo were “insufferable” and “horrible” in the build-up to the film’s release. Grande, 31, also said her co-star was a “brilliant gift of a human being” while being interviewed by Gladiator star Paul Mescal for US news outlet Variety. Mescal told the singer and actor: “I’m watching you guys in the press tour. You’re obviously in love with each other.” To which she replied: “Insufferable. Yes. We’re horrible. It’s bad.” The 7 Rings singer plays Glinda, while her 37-year-old co-star plays Elphaba, in the film which is an adaption of the musical stage show of the same name and is set in The Land Of Oz before the events of The Wizard Of Oz. Their interviews for the film, which have seen the two being emotional towards one another and holding hands, have gone viral on social media. Speaking about Erivo, Grande said: “Cynthia is just an absolute brilliant gift of a human being. I think we tried to keep the pressure out of the room, obviously, as much as possible.” She also said she had not had any read throughs with her co-star before joining the cast. Grande said: “We never chemistry read together, it was three rounds for me, and I read with two different actresses. “I stayed for three and a half hours the final day, and I had cried so much. “We did Popular, Defying Gravity, (and) For Good (songs from the film), and I left my lashes on the mirror, because I left everything else in the room.” The film follows Elphaba, who is misunderstood because of her green skin, as she forges an unlikely friendship with Glinda, a student with a desire for popularity. Bullying of the green-skinned witch saw the movie, which also stars Peter Dinklage, Jeff Goldblum and Jonathan Bailey, given a PG rating by the British Board Of Film Classification (BBFC) for “discrimination”.

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