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10000DAYS canlı yayında!  Merhaba, anknoarhua! 10000DAYS canlı yayında!  RANK | ABONE REKORU KIRIYORUZ! 6600 ABONE! | YENİ KUTU İÇİN >❗PrimePubg 💜 | ❗incehesap !yemeksepetiPUBG: BATTLEGROUNDS adlı oyun yayınlanıyor Şimdi İzleVeya şu bağlantıyı tıklayın: https://www.twitch.tv/10000days  Bu e-postayı, Twitch topluluğunun değerli bir üyesi olduğunuz için alıyorsunuz.10000DAYS ile ilgili artık e-posta almak istemiyorsanız burayı tıklayın.Takip ettiğiniz bir kanal yayına girdiğinde artık e-posta almak istemiyorsanız burayı tıklayın.© 2023 Twitch, Tüm Hakları Saklıdır350 Bush Street, 2nd Floor, San Francisco, CA, 94104 – USA
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[Image “Header+-+AGI.png”] Artificial General Intelligence (AGI) is a term for Artificial Intelligence systems that meet or exceed human performance on the broad range of tasks that humans are capable of performing. There are benefits and downsides to AGI. On the upside, AGIs can do most of the labor that consumes a vast amount of humanity’s time and energy. AGI can herald a utopia where no one has wants that cannot be fulfilled. AGI can also result in an unbalanced situation where one (or a few) companies dominate the economy, exacerbating the existing dichotomy between the top 1% and the rest of humankind. Beyond that, the argument goes, a super-intelligent AGI could find it beneficial to enslave humans for its own purposes, or exterminate humans so as to not compete for resources. One hypothetical scenario is that an AGI that is smarter than humans can simply design a better AGI, which can, in turn, design an even better AGI, leading to something called hard take-off and the singularity. I do not know of any theory that claims that AGI or the singularity is impossible. However, I am generally skeptical of arguments that Large Language Models such the GPT series (GPT-2, GPT-3, GPT-4, GPT-X) are on the pathway to AGI. This article will attempt to explain why I believe that to be the case, and what I think is missing should humanity (or members of the human race) so choose to try to achieve AGI. I will also try to convey a sense of why it is easy to talk about the so-called “recipe for AGI” in the abstract but why physics itself will prevent any sudden and unexpected leap from where we are now to AGI or super-AGI. What Is Missing? To achieve AGI it seems likely we will need one or more of the following: – Online non-greedy planning technologies that can operate in the presence of a priori unseen tasks, states, and actions. This may be reinforcement learning or something else. The challenge here is to be able to represent and reason about states and actions that are task agnostic or can change the representations based on tasks that have never been seen before. We may also need the ability to generalize or transfer across unseen states, actions, and tasks to reduce the amount of costly exploration of possible future states and actions. However, there is no way to completely eliminate the need for exploration. – A world model technology that robustly predicts state-action-state transitions for all states and actions that might be encountered while performing any task in any environment. Such a world model would enable fast online planning (using reinforcement learning or otherwise). To learn such a world model would require data of real-world state-action transitions, which in turn will require the ability to explore and interact with the real world. – If Large Language Models (LLMs) are to be used in AGI, we may require one of the following: (a) a reinforcement learning agent that wraps an LLM in a traditional trial-and-error training loop; (b) a reinforcement learning from human feedback technique that factors in states and goals and rewards for successful transitions and goal achievement. The three missing capabilities are inexorably linked. Planning is a process of deciding which actions to perform to achieve a goal. Reinforcement learning — the current favorite path forward — requires exploration of the real world to learn how to plan, and/or the ability to imagine how the world will change when it tries different actions. A world model can predict how the world will change when an agent attempts to perform an action. But world models are best learned through exploration. — Read the rest on Medium [Image “CTA+-+Black.png”]
[Image “Header+-+AGI.png”] Artificial General Intelligence (AGI) is a term for Artificial Intelligence systems that meet or exceed human performance on the broad range of tasks that humans are capable of performing. There are benefits and downsides to AGI. On the upside, AGIs can do most of the labor that consumes a vast amount of humanity’s time and energy. AGI can herald a utopia where no one has wants that cannot be fulfilled. AGI can also result in an unbalanced situation where one (or a few) companies dominate the economy, exacerbating the existing dichotomy between the top 1% and the rest of humankind. Beyond that, the argument goes, a super-intelligent AGI could find it beneficial to enslave humans for its own purposes, or exterminate humans so as to not compete for resources. One hypothetical scenario is that an AGI that is smarter than humans can simply design a better AGI, which can, in turn, design an even better AGI, leading to something called hard take-off and the singularity. I do not know of any theory that claims that AGI or the singularity is impossible. However, I am generally skeptical of arguments that Large Language Models such the GPT series (GPT-2, GPT-3, GPT-4, GPT-X) are on the pathway to AGI. This article will attempt to explain why I believe that to be the case, and what I think is missing should humanity (or members of the human race) so choose to try to achieve AGI. I will also try to convey a sense of why it is easy to talk about the so-called “recipe for AGI” in the abstract but why physics itself will prevent any sudden and unexpected leap from where we are now to AGI or super-AGI. What Is Missing? To achieve AGI it seems likely we will need one or more of the following: – Online non-greedy planning technologies that can operate in the presence of a priori unseen tasks, states, and actions. This may be reinforcement learning or something else. The challenge here is to be able to represent and reason about states and actions that are task agnostic or can change the representations based on tasks that have never been seen before. We may also need the ability to generalize or transfer across unseen states, actions, and tasks to reduce the amount of costly exploration of possible future states and actions. However, there is no way to completely eliminate the need for exploration. – A world model technology that robustly predicts state-action-state transitions for all states and actions that might be encountered while performing any task in any environment. Such a world model would enable fast online planning (using reinforcement learning or otherwise). To learn such a world model would require data of real-world state-action transitions, which in turn will require the ability to explore and interact with the real world. – If Large Language Models (LLMs) are to be used in AGI, we may require one of the following: (a) a reinforcement learning agent that wraps an LLM in a traditional trial-and-error training loop; (b) a reinforcement learning from human feedback technique that factors in states and goals and rewards for successful transitions and goal achievement. The three missing capabilities are inexorably linked. Planning is a process of deciding which actions to perform to achieve a goal. Reinforcement learning — the current favorite path forward — requires exploration of the real world to learn how to plan, and/or the ability to imagine how the world will change when it tries different actions. A world model can predict how the world will change when an agent attempts to perform an action. But world models are best learned through exploration. — Read the rest on Medium [Image “CTA+-+Black.png”]
Mithrain canlı yayında!  Merhaba, anknoarhua! Mithrain canlı yayında!  TURNUVA YAYINI 21:00’DE!!!!Escape from Tarkov adlı oyun yayınlanıyor Şimdi İzleVeya şu bağlantıyı tıklayın: https://www.twitch.tv/mithrain  Bu e-postayı, Twitch topluluğunun değerli bir üyesi olduğunuz için alıyorsunuz.Mithrain ile ilgili artık e-posta almak istemiyorsanız burayı tıklayın.Takip ettiğiniz bir kanal yayına girdiğinde artık e-posta almak istemiyorsanız burayı tıklayın.Bu içeriği şikayet etmek için buraya tıklayın.© 2023 Twitch, Tüm Hakları Saklıdır350 Bush Street, 2nd Floor, San Francisco, CA, 94104 – USA
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Konak’ta palmiyelere enjeksiyonlu koruma

Konak’ta palmiyelere enjeksiyonlu koruma
  • 13 Aralık 2020 12:46
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Konak Belediyesi Park ve Bahçeler Müdürlüğü ekipleri, ilçe sınırları içinde yer alan palmiyeleri kırmızı böceğe karşı korumak için yoğun bir çalışma yürütüyor. Ekipler düzenli ilaçlamanın yanı sıra zarar gören palmiyeler için enjeksiyon yöntemi ile tam koruma sağlıyor.

 

Konak’ta palmiyelere enjeksiyonlu koruma

Konak Belediyesi Park ve Bahçeler Müdürlüğü’ne bağlı ekipler, İzmir’in sembolü palmiyelere gözü gibi bakıyor. 45 günlük aralıklarla İzmir’in gözbebeği palmiyeleri düzenli olarak ilaçlayan ekipler, “kırmızı böcek” başta olmak üzere zararlı böceklerle mücadele ediyor. Konak Belediyesi ekipleri, ilçe sınırları içinde palmiyelerin yoğun olarak bulunduğu Alsancak Sevgi Yolu, Güzelyalı Fuat Göztepe Parkı, Mehmet Ali Akman Parkı, Ali Rıza Akıncı Parkı, Zeytinlik Parkı ile Çınarlı Rekreasyon Alanı’nda bulunan palmiyelerin, budama ve ilaçlama olmak üzere tüm bakımlarını düzenli olarak yapıyor. Palmiyelerin böcekler tarafından istila edilmeden, hastalanmadan önce gerekli tüm tedbirleri almak için yoğun çaba gösteren Konak Belediyesi ekipleri, yapılan tüm çalışmalara karşın zarar gören palmiyeler için ayrı bir bakım programı ve şekli uyguluyor.Konak’ta palmiyelere enjeksiyonlu koruma

Kırmızı böceklere, enjeksiyonlu ilaçlama

Konak Belediyesi Park ve Bahçeler Müdürlüğü ekipleri, palmiyelerin özellikle Phoenix türünde daha çok görülen “kırmızı böcek” ile yoğun mücadele ediyor. Palmiyelerin tepesinden giren kırmızı böceğin, ağacın gövdesine ilerleyerek larvalarını bırakması nedeniyle, böcekle daha etkin bir mücadele yöntemi olan enjeksiyonlu ilaçlama yapan ekipler, işlerini şansa bırakmıyor. Ağacın gövdesine takılan sonda ile ilaç enjekte edilerek, ağaç gövdesine bırakılan larvaların yaşamasına fırsat verilmiyor. Öte yandan kırmızı böceğin girdiği tespit edilen palmiyelere bom kullanılarak tepeden ilaçlama da yapılıyor. Belediye ekipleri, kırmızı böceğin girdiği tespit edilen palmiyeleri, son larvanın yok olduğu güne kadar on beş günde bir aynı yöntemle ilaçlıyor.

 

Konunun uzmanları tarafından mücadele edilmesi zor bir böcek türü olarak gösterilen kırmızı böceğe karşı koruma amaçlı ilaçlamaları aksatmayan Konak Belediyesi’ne bağlı ekipler, 2020 yılı içinde elli beş palmiyenin kırmızı böcek sebepli kaybedildiği bilgisini verdiler.

Konak’ta palmiyelere enjeksiyonlu koruma

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