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Report declares iPhone 16 launch on September 10

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Renders of the iPhone 16 and iPhone 16 Professional


Report declares iPhone 16 launch on September 10

Following our authentic report placing the iPhone 16 and different {hardware} occasion on September 10, one other supply is confirming the date and launch schedule.

September is the standard month for Apple’s fall releases to be launched, together with its flagship launches just like the iPhone 16 machine vary. Whereas Apple has but to really affirm when the occasion will happen, a report on Friday makes the declare that it’ll occur on September 10.

Friday’s report from Bloomberg cites “individuals accustomed to the scenario” in marking the date for the launch. Regardless of an absence of an announcement as of but, the report says that Apple is “making preparations for that date.”

After the occasion itself, Apple shall be placing its new merchandise on sale ranging from September 20, the report provides. The discharge record will embody iPhones, AirPods, and the Apple Watch Collection 10.

What in all probability will not make it to the occasion are Macs, which are likely to arrive a month later throughout an October occasion.

Guessable timing

Whereas it’s getting fairly near the interval that observers would count on for the launch occasion to happen, the report is not fully stunning.

The September 10 date, as beforehand mentioned at size by AppleInsider, is anticipated by many for plenty of causes. This consists of issues like Apple preferring to launch merchandise on a Tuesday in early September.

Since September 3 follows Labor Day, that makes September 10 a extra probably prospect.

What the report does do is provide a declare that somebody in or shut sufficient to Apple is allegedly conscious that the date is formally September 10.

Nonetheless, regardless of the educated guesswork of the rumor mill and the overall perception of it taking place on September 10, it is fully doable for Apple to make use of a distinct date fully.

No-one will actually know when the Apple Occasion shall be outdoors of the corporate till invites get despatched out. Actual ones this time.

How AI is Revolutionizing the Method We Plan Holidays

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Synthetic Intelligence (AI) has reworked quite a few industries, and journey is not any exception. From customized suggestions to sensible itinerary planning, AI has made vacation planning extra handy, environment friendly, and tailor-made to particular person preferences. On this article, we discover how AI is revolutionizing the best way we plan holidays, and why it is turning into a vital instrument for vacationers.

Personalised Journey Suggestions

One of the crucial important impacts of AI in journey planning is the power to supply customized suggestions. AI algorithms analyze person information, equivalent to previous journey experiences, preferences, and on-line habits, to counsel locations, actions, and lodging that match particular person tastes. Whether or not you are searching for a cultural journey or a soothing seashore getaway, AI might help you discover the right vacation spot.

Simplifying Flight Bookings

Reserving flights has by no means been simpler, due to AI-powered platforms that permit vacationers to match costs, discover one of the best offers, and guide on-line flights in only a few clicks. AI instruments can predict value fluctuations and advocate one of the best time to guide, guaranteeing you get probably the most worth to your cash. For example, if you happen to’re planning holidays from Newquay Airport, AI might help you uncover probably the most inexpensive and handy flight choices, making all the course of stress-free.

Sensible Itinerary Planning

Gone are the times of manually making a journey itinerary. AI-powered journey apps can now mechanically generate itineraries based mostly in your pursuits and journey dates. These apps take into account elements equivalent to climate circumstances, common points of interest, and native occasions to craft a customized schedule that maximizes your vacation expertise. This sensible planning ensures that you simply take advantage of your time and do not miss out on must-see spots.

Enhancing Buyer Service with Chatbots

AI-driven chatbots have turn out to be a staple within the journey trade, providing 24/7 buyer assist. Whether or not you want help with reserving modifications, journey inquiries, or suggestions, AI chatbots present fast and correct responses. This real-time assist enhances the journey expertise, particularly once you’re on the go and want instantaneous assist.

Digital Journey Assistants

Digital journey assistants powered by AI are one other game-changer. These digital companions can handle your complete journey, from reserving flights and inns to offering real-time updates on flight standing and native points of interest. They’ll even make restaurant reservations and counsel hidden gems based mostly in your preferences. With AI digital assistants, you may take pleasure in a seamless and stress-free vacation expertise.

Predictive Analytics for Higher Journey Selections

AI’s predictive analytics capabilities have revolutionized journey planning by serving to vacationers make knowledgeable choices. By analyzing historic information and present traits, AI can predict climate circumstances, flight delays, and even one of the best occasions to go to particular locations. This foresight allows vacationers to plan their holidays extra successfully and keep away from potential disruptions.

Conclusion

AI has undoubtedly revolutionized the best way we plan holidays, providing customized experiences, simplifying the reserving course of, and enhancing total comfort. Whether or not you are trying to guide on-line flights or plan holidays from Newquay Airport, AI-driven instruments are making journey planning extra accessible and pleasing than ever earlier than. As AI continues to evolve, its function within the journey trade is about to turn out to be much more outstanding, shaping the way forward for vacation planning.

The put up How AI is Revolutionizing the Method We Plan Holidays appeared first on Datafloq.

Understanding Reminiscence Consistency in Java Threads


Java Programming tutorialsJava Programming tutorials

Java, as a flexible and widely-used programming language, offers assist for multithreading, permitting builders to create concurrent functions that may execute a number of duties concurrently. Nonetheless, with the advantages of concurrency come challenges, and one of many important features to contemplate is reminiscence consistency in Java threads.

In a multithreaded setting, a number of threads share the identical reminiscence house, resulting in potential points associated to knowledge visibility and consistency. Reminiscence consistency refers back to the order and visibility of reminiscence operations throughout a number of threads. In Java, the Java Reminiscence Mannequin (JMM) defines the foundations and ensures for a way threads work together with reminiscence, guaranteeing a stage of consistency that enables for dependable and predictable conduct.

Learn: High On-line Programs for Java

How Does Reminiscence Consistency in Java Work?

Understanding reminiscence consistency includes greedy ideas like atomicity, visibility, and ordering of operations. Let’s delve into these features to get a clearer image.

Atomicity

Within the context of multithreading, atomicity refers back to the indivisibility of an operation. An atomic operation is one which seems to happen instantaneously, with none interleaved operations from different threads. In Java, sure operations, reminiscent of studying or writing to primitive variables (besides lengthy and double), are assured to be atomic. Nonetheless, compound actions, like incrementing a non-volatile lengthy, are usually not atomic.

Here’s a code instance demonstrating atomicity:

public class AtomicityExample {

    non-public int counter = 0;
    public void increment() {
        counter++; // Not atomic for lengthy or double
    }
    public int getCounter() {
        return counter; // Atomic for int (and different primitive varieties besides lengthy and double)
    }
}

For atomic operations on lengthy and double, Java offers the java.util.concurrent.atomic bundle with courses like AtomicLong and AtomicDouble, as proven under:

import java.util.concurrent.atomic.AtomicLong;

 

public class AtomicExample {

    non-public AtomicLong atomicCounter = new AtomicLong(0);

 

    public void increment() {

        atomicCounter.incrementAndGet(); // Atomic operation

    }

 

    public lengthy getCounter() {

        return atomicCounter.get(); // Atomic operation

    }

}

Visibility

Visibility refers as to if adjustments made by one thread to shared variables are seen to different threads. In a multithreaded setting, threads could cache variables regionally, resulting in conditions the place adjustments made by one thread are usually not instantly seen to others. To handle this, Java offers the risky key phrase.

public class VisibilityExample {

    non-public risky boolean flag = false;




    public void setFlag() {

        flag = true; // Seen to different threads instantly

    }




    public boolean isFlag() {

        return flag; // All the time reads the newest worth from reminiscence

    }

}

Utilizing risky ensures that any thread studying the variable sees the latest write.

Ordering

Ordering pertains to the sequence through which operations seem like executed. In a multithreaded setting, the order through which statements are executed by totally different threads could not all the time match the order through which they had been written within the code. The Java Reminiscence Mannequin defines guidelines for establishing a happens-before relationship, guaranteeing a constant order of operations.

public class OrderingExample {

    non-public int x = 0;

    non-public boolean prepared = false;




    public void write() {

        x = 42;

        prepared = true;

    }




    public int learn() {

        whereas (!prepared) {

            // Spin till prepared

        }

        return x; // Assured to see the write due to happens-before relationship

    }

}

By understanding these fundamental ideas of atomicity, visibility, and ordering, builders can write thread-safe code and keep away from widespread pitfalls associated to reminiscence consistency.

Learn: Finest Practices for Multithreading in Java

Thread Synchronization

Java offers synchronization mechanisms to manage entry to shared assets and guarantee reminiscence consistency. The 2 most important synchronization mechanisms are synchronized strategies/blocks and the java.util.concurrent bundle.

Synchronized Strategies and Blocks

The synchronized key phrase ensures that just one thread can execute a synchronized technique or block at a time, stopping concurrent entry and sustaining reminiscence consistency. Right here is an quick code instance demonstrating methods to use the synchronized key phrase in Java:

public class SynchronizationExample {

    non-public int sharedData = 0;




    public synchronized void synchronizedMethod() {

        // Entry and modify sharedData safely

    }




    public void nonSynchronizedMethod() {

        synchronized (this) {

            // Entry and modify sharedData safely

        }

    }

}

Whereas synchronized offers an easy option to obtain synchronization, it might probably result in efficiency points in sure conditions as a consequence of its inherent locking mechanism.

java.util.concurrent Package deal

The java.util.concurrent bundle introduces extra versatile and granular synchronization mechanisms, reminiscent of Locks, Semaphores, and CountDownLatch. These courses provide higher management over concurrency and could be extra environment friendly than conventional synchronization.

import java.util.concurrent.locks.Lock;

import java.util.concurrent.locks.ReentrantLock;




public class LockExample {

    non-public int sharedData = 0;

    non-public Lock lock = new ReentrantLock();




    public void performOperation() {

        lock.lock();

        strive {

            // Entry and modify sharedData safely

        } lastly {

            lock.unlock();

        }

    }

}

Utilizing locks permits for extra fine-grained management over synchronization and may result in improved efficiency in conditions the place conventional synchronization could be too coarse.

Reminiscence Consistency Ensures

The Java Reminiscence Mannequin offers a number of ensures to make sure reminiscence consistency and a constant and predictable order of execution for operations in multithreaded packages:

  1. Program Order Rule: Every motion in a thread happens-before each motion in that thread that comes later in this system order.
  2. Monitor Lock Rule: An unlock on a monitor happens-before each subsequent lock on that monitor.
  3. Unstable Variable Rule: A write to a risky subject happens-before each subsequent learn of that subject.
  4. Thread Begin Rule: A name to Thread.begin on a thread happens-before any motion within the began thread.
  5. Thread Termination Rule: Any motion in a thread happens-before some other thread detects that thread has terminated.

Sensible Suggestions for Managing Reminiscence Consistency

Now that we’ve lined the basics, let’s discover some sensible suggestions for managing reminiscence consistency in Java threads.

1. Use risky Correctly

Whereas risky ensures visibility, it doesn’t present atomicity for compound actions. Use risky judiciously for easy flags or variables the place atomicity shouldn’t be a priority.

public class VolatileExample {

    non-public risky boolean flag = false;




    public void setFlag() {

        flag = true; // Seen to different threads instantly, however not atomic

    }




    public boolean isFlag() {

        return flag; // All the time reads the newest worth from reminiscence

    }

}

2. Make use of Thread-Protected Collections

Java offers thread-safe implementations of widespread assortment courses within the java.util.concurrent bundle, reminiscent of ConcurrentHashMap and CopyOnWriteArrayList. Utilizing these courses can eradicate the necessity for specific synchronization in lots of circumstances.

import java.util.Map;

import java.util.concurrent.ConcurrentHashMap;




public class ConcurrentHashMapExample {

    non-public Map Integer> concurrentMap = new ConcurrentHashMap<>();




    public void addToMap(String key, int worth) {

        concurrentMap.put(key, worth); // Thread-safe operation

    }




    public int getValue(String key) {

        return concurrentMap.getOrDefault(key, 0); // Thread-safe operation

    }

}

You may be taught extra about thread-safe operations in our tutorial: Java Thread Security.

3. Atomic Lessons for Atomic Operations

For atomic operations on variables like int and lengthy, think about using courses from the java.util.concurrent.atomic bundle, reminiscent of AtomicInteger and AtomicLong.

import java.util.concurrent.atomic.AtomicInteger;




public class AtomicIntegerExample {

    non-public AtomicInteger atomicCounter = new AtomicInteger(0);




    public void increment() {

        atomicCounter.incrementAndGet(); // Atomic operation

    }




    public int getCounter() {

        return atomicCounter.get(); // Atomic operation

    }

}

4. Fantastic-Grained Locking

As an alternative of utilizing coarse-grained synchronization with synchronized strategies, think about using finer-grained locks to enhance concurrency and efficiency.

import java.util.concurrent.locks.Lock;

import java.util.concurrent.locks.ReentrantLock;


public class FineGrainedLockingExample {

    non-public int sharedData = 0;

    non-public Lock lock = new ReentrantLock();

    public void performOperation() {

        lock.lock();

        strive {

            // Entry and modify sharedData safely

        } lastly {

            lock.unlock();

        }

    }

}

5. Perceive the Occurs-Earlier than Relationship

Pay attention to the happens-before relationship outlined by the Java Reminiscence Mannequin (see the Reminiscence Consistency Ensures part above.) Understanding these relationships helps in writing right and predictable multithreaded code.

Remaining Ideas on Reminiscence Consistency in Java Threads

Reminiscence consistency in Java threads is a important side of multithreaded programming. Builders want to pay attention to the Java Reminiscence Mannequin, perceive the ensures it offers, and make use of synchronization mechanisms judiciously. By utilizing methods like risky for visibility, locks for fine-grained management, and atomic courses for particular operations, builders can guarantee reminiscence consistency of their concurrent Java functions.

Learn: Finest Java Refactoring Instruments

AI21 Labs Launched Jamba 1.5 Household of Open Fashions: Jamba 1.5 Mini and Jamba 1.5 Massive Redefining Lengthy-Context AI with Unmatched Velocity, High quality, and Multilingual Capabilities for International Enterprises


AI21 Labs has made a big stride within the AI panorama by releasing the Jamba 1.5 household of open fashions, comprising Jamba 1.5 Mini and Jamba 1.5 Massive. These fashions, constructed on the novel SSM-Transformer structure, characterize a breakthrough in AI expertise, significantly in dealing with long-context duties. AI21 Labs goals to democratize entry to those highly effective fashions by releasing them underneath the Jamba Open Mannequin License, encouraging widespread experimentation and innovation.

Key Options of the Jamba 1.5 Fashions

One of many standout options of the Jamba 1.5 fashions is their skill to deal with exceptionally lengthy contexts. They boast an efficient context window of 256K tokens, the longest available in the market for open fashions. This function is essential for enterprise purposes requiring the evaluation and summarization of prolonged paperwork. The fashions additionally excel in agentic and Retrieval-Augmented Technology (RAG) workflows, enhancing each the standard and effectivity of those processes.

Concerning pace, the Jamba 1.5 fashions are as much as 2.5 instances quicker on lengthy contexts than their opponents, and so they keep superior efficiency throughout all context lengths inside their dimension class. This pace benefit is essential for enterprises that want speedy turnaround instances for duties similar to buyer assist or large-scale information processing.

The standard of the Jamba 1.5 fashions is one other space the place they outshine their friends. Jamba 1.5 Mini has been acknowledged because the strongest open mannequin in its dimension class, reaching a rating of 46.1 on the Enviornment Onerous benchmark, outperforming bigger fashions like Mixtral 8x22B and Command-R+. Jamba 1.5 Massive goes even additional, scoring 65.4, which surpasses main fashions similar to Llama 3.1 70B and 405B. This high-quality efficiency throughout totally different benchmarks highlights the robustness of the Jamba 1.5 fashions in delivering dependable and correct outcomes.

Multilingual Assist and Developer Readiness

Along with their technical prowess, the Jamba 1.5 fashions are designed with multilingual assist, catering to languages similar to Spanish, French, Portuguese, Italian, Dutch, German, Arabic, and Hebrew. This makes them versatile instruments for world enterprises working in numerous linguistic environments.

For builders, Jamba 1.5 fashions provide native assist for structured JSON output, operate calling, doc object digestion, and quotation era. These options make the fashions adaptable to varied improvement wants, enabling seamless integration into present workflows.

Deployment and Effectivity

AI21 Labs has ensured that the Jamba 1.5 fashions are accessible and deployable throughout a number of platforms. They’re out there for speedy obtain on Hugging Face and are supported by main cloud suppliers, together with Google Cloud Vertex AI, Microsoft Azure, and NVIDIA NIM. The fashions are anticipated to be out there quickly on further platforms similar to Amazon Bedrock, Databricks Market, Snowflake Cortex, and others, making them simply deployable in varied environments, together with on-premises and digital non-public clouds.

One other essential benefit of the Jamba 1.5 fashions is their useful resource effectivity. Constructed on a hybrid structure that mixes the strengths of Transformer and Mamba architectures, these fashions provide a decrease reminiscence footprint, permitting enterprises to deal with intensive context lengths on a single GPU. AI21 Labs’ novel quantization approach, ExpertsInt8, additional enhances this effectivity, which optimizes mannequin efficiency with out compromising high quality.

Conclusion

The discharge of the Jamba 1.5 household by AI21 Labs marks a big development in long-context dealing with. These fashions set new benchmarks in pace, high quality, and effectivity and democratize entry to cutting-edge AI expertise by means of their open mannequin license. As enterprises proceed to hunt AI options that ship real-world worth, the Jamba 1.5 fashions stand out as highly effective instruments able to assembly the calls for of complicated, large-scale purposes. Their availability throughout a number of platforms and assist for multilingual environments additional improve their attraction, making them a flexible alternative for builders and companies.


Take a look at the Jamba 1.5 mini, Jamba 1.5 massive, and Particulars. All credit score for this analysis goes to the researchers of this challenge. Additionally, don’t neglect to comply with us on Twitter and be part of our Telegram Channel and LinkedIn Group. In the event you like our work, you’ll love our publication..

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Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is dedicated to harnessing the potential of Synthetic Intelligence for social good. His most up-to-date endeavor is the launch of an Synthetic Intelligence Media Platform, Marktechpost, which stands out for its in-depth protection of machine studying and deep studying information that’s each technically sound and simply comprehensible by a large viewers. The platform boasts of over 2 million month-to-month views, illustrating its recognition amongst audiences.



California is the robotics capital of the world

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I got here to the Silicon Valley area in 2010 as a result of I knew it was the robotics heart of the world, but it surely actually doesn’t get wherever close to the media consideration that another robotics areas do. In California, robotics know-how is a small fish in a a lot larger know-how pond, and that tends to hide how essential Californian corporations are to the robotics revolution.

This conservative dataset from Pitchbook [Vertical: Robotics and Drones] gives information for 7166 robotics and drones corporations, though a extra personalized search would supply nearer to 10,000 robotics corporations world large. Areas ordered by dimension are:

  • North America 2802
  • Asia 2337
  • Europe 2285
  • Center East 321
  • Oceania 155
  • South America 111
  • Africa 63
  • Central America 13

 

  1. California = 843 (667) * no of corporations adopted by no of head quarters
  2. Texas = 220 (159)
  3. New York = 193 (121)
  4. Massachusetts = 191 (135)
  5. Florida = 136 (95)
  6. Pennsylvania = 113 (89)
  7. Washington = 85 (61)
  8. Colorado = 83 (57)
  9. Virginia = 81 (61)
  10. Michigan = 70 (56)
  11. Illinois = 66 (43)
  12. Ohio = 65 (56)
  13. Georgia = 64 (46)
  14. New Jersey = 53 (36)
  15. Delaware = 49 (18)
  16. Maryland = 48 (34)
  17. Arizona = 48 (37)
  18. Nevada = 42 (29)
  19. North Carolina = 39 (29)
  20. Minnesota = 31 (25)
  21. Utah = 30 (24)
  22. Indiana = 29 (26)
  23. Oregon = 29 (20)
  24. Connecticut = 27 (22)
  25. DC = 26 (12)
  26. Alabama = 25 (21)
  27. Tennessee = 20 (18)
  28. Iowa = 17 (14)
  29. New Mexico = 17 (15)
  30. Missouri = 17 (16)
  31. Wisconsin = 15 (12)
  32. North Dakota = 14 (8)
  33. South Carolina = 13 (11)
  34. New Hampshire = 13 (12)
  35. Nebraska = 13 (11)
  36. Oklahoma = 10 (8)
  37. Kentucky = 10 (7)
  38. Kansas = 9 (9)
  39. Louisiana = 9 (8)
  40. Rhode Island = 8 (6)
  41. Idaho = 8 (6)
  42. Maine = 5 (5)
  43. Montana = 5 (4)
  44. Wyoming = 5 (3)
  45. Mississippi = 3 (1)
  46. Arkansas = 3 (2)
  47. Alaska = 3 (3)
  48. Hawaii = 2 (1)
  49. West Virginia = 1 (1)
  50. South Dakota = 1 (0)

Notice – this quantity in brackets is for HQ places, whereas the primary quantity is for all firm places. The top outcomes and rankings are virtually the identical.

 

  1. China = 1350
  2. Japan = 283
  3. India = 261
  4. South Korea = 246
  5. Israel = 193
  6. Hong Kong = 72
  7. Russia = 69
  8. United Arab Emirates = 50
  9. Turkey = 48
  10. Malaysia = 35
  11. Taiwan = 21
  12. Saudi Arabia = 19
  13. Thailand = 13
  14. Vietnam = 12
  15. Indonesia = 10
  16. Lebanon = 7
  17. Kazakhstan = 3
  18. Iran = 3
  19. Kuwait = 3
  20. Oman = 3
  21. Qatar = 3
  22. Pakistan = 3
  23. Philippines = 2
  24. Bahrain = 2
  25. Georgia = 2
  26. Sri Lanka = 2
  27. Azerbaijan = 1
  28. Nepal = 1
  29. Armenia = 1
  30. Burma/Myanmar = 1

Nations with no robotics; Yemen, Iraq, Syria, Turkmenistan, Afghanistan, Syria, Jordan, Uzbekistan, Kyrgyzstan, Tajikistan, Bangladesh, Bhutan, Mongolia, Cambodia, Laos, North Korea, East Timor.

 

  1. United Kingdom = 443
  2. Germany = 331
  3. France = 320
  4. Spain = 159
  5. Netherlands = 156
  6. Switzerland = 140
  7. Italy = 125
  8. Denmark = 115
  9. Sweden = 85
  10. Norway = 80
  11. Poland = 74
  12. Belgium = 72
  13. Russia = 69
  14. Austria = 51
  15. Turkey = 48
  16. Finland = 45
  17. Portugal = 36
  18. Eire = 28
  19. Estonia = 24
  20. Ukraine = 22
  21. Czech Republic = 19
  22. Romania = 19
  23. Hungary = 18
  24. Lithuania = 18
  25. Latvia = 15
  26. Greece = 15
  27. Bulgaria = 11
  28. Slovakia = 10
  29. Croatia = 7
  30. Slovenia = 6
  31. Serbia = 6
  32. Belarus = 4
  33. Iceland = 3
  34. Cyprus = 2
  35. Bosnia & Herzegovina = 1

Nations with no robotics; Andorra, Montenegro, Albania, Macedonia, Kosovo, Moldova, Malta, Vatican Metropolis.

 

  1. Ontario = 144
  2. British Colombia = 60
  3. Quebec = 53
  4. Alberta = 34
  5. Manitoba = 7
  6. Saskatchewan = 6
  7. Newfoundland & Labrador = 2
  8. Yukon = 1

Areas with no robotics; Nunavut, Northwest Territories.




Silicon Valley Robotics
is an business affiliation supporting innovation and commercialization of robotics applied sciences.

Silicon Valley Robotics
is an business affiliation supporting innovation and commercialization of robotics applied sciences.