In most cases, R is better than Python when it comes to statistics. AI is a high-tech field where the maximum level of knowledge is required. After all, creating products that think and act like humans is not an easy task.
- Programming languages are the tools to build and control a model.
- With hundreds of ML programming languages to choose from, selecting the best option for machine learning projects can be difficult.
- Prolog is still used in IBM Watson and some NLP applications.
- You don’t even require any prior machine learning experience to do it.
- TensorFlow is Python’s most popular framework for AI development.
- Machine learning engineers are among the most sought-after employees in the technology sector right now.
Rust works at the backend of many well-known best languages for ai like Dropbox, Yelp, Firefox, Azure, Polkadot, Cloudflare, npm, Discord, etc. The memory safety, speed, and ease of expression make Rust the perfect choice for AI development and scientific computing. Some successful projects made with Lisp are Routinic, Grammarly, and DART. Though it has its drawbacks, Lisp is still a promising programming language for AI development. Even when it comes to natural language processing which is so integral to user experience today, Python has an edge thanks to tools like SpaCy and NTLK.
Python Programming Language
Developing intuitive AI systems that give users an ethereal experience hinges on using the right programming language for AI. There are numerous programming languages out there, each with its own merits and areas of strength. Which programming language you use for your AI project depends on specific requirements. Some AI programming languages excel at handling large swathes of data and crunching big numbers, others shine at natural language programming. Understanding the strengths and specifics of each language will help you determine the perfect fit for your project. Java is a versatile and powerful programming language that enables developers to create robust, high-performance applications.
If you’re creating an application that will be used on more than one platform, that can affect your choice of language. You may need different platform compilers and executables if you choose one language, but another may be able to handle it in a simpler manner. JavaScript has a dedicated community of loyal fans who contribute to its resources. Libraries like jQuery, React.js, and Underscore.js help make AI development quicker and easier. Although the potential for AI is vast, you’ll need software engineers who are proficient in the best languages for AI in order to realize it.
Some benefits of Python for AI Programming:
Scikit-learn supports fundamental machine learning algorithms like classification and regression, while Keras, Caffe, and TensorFlow facilitate deep learning. Due to its straightforward structure and text processing tools like NTLK and SpaCy, Python is a top-choice programming language for natural language processing. Scala has a number of libraries and frameworks for AI development, including Apache Spark, Deeplearning4j, and ScalaNLP.
Which Language is Best for AI?
Python is the best language for AI. It is easy to learn, has a large community of developers, and has an extensive collection of frameworks, libraries, and codebases. However, Python has some criticisms—it can be slow, and its loose syntax may teach programmers bad habits.
Our developers have been rigorously screened for their tech skills as well as their English skills. They’re from Latin American countries that have a significant timezone overlap with the U.S., so you don’t have to worry about real-time collaboration conflicts. Reach out to our expert team today to find out how Revelo can help you grow your AI development team. You want your program to look and behave consistently across all platforms.
#4 C++
Real-world applications of artificial intelligence in web design. Rather than mimicking human intelligence, the concepts of machine learning apply one of the key behaviors of humans – the ability to learn. As the algorithm learns more, its predictive modeling becomes more accurate and therefore more valuable for the people using them.
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jiUlJ1YzgeReyiN9yVLBorCiJ3Jz1rpVsQyopamx3ikZ8KufspaKkFaTnFdLBOE37LsVOEoe8qJ/YSW4SRnoKivaBe2WobjAV4lAgCpJb3S1b1Efuap7WM9yTdXA4ThJwKbmlwRbFw3ZGHQrbwKbcbvFRYsFTe4DNDHkbXCCMVyZdTSmcLQCMigVwTtdNGXFlPnQSeoqezmsuZ2ggnZD+wl+5RpLS+nXtS3pcFtzu0o3LWrqQM+Q+unNnZKYC1D90TRXspKGtVOOqWQcKAT6n3+6kYEsmRJl5bjEF6r0u/peaiO493qHUlSFYwcZ86D7k1ZXarbplznRXokdxzakhQA4FQT7nbz/m2R+Ia1ZcbhNpLYVCVq2TOsHWsrBXmD3xsjzrQrecitUNEO6ysrKgYwvv+Jp38gv8ARVXVaN9/xNO/kF/oqrq6eh9x+p5ntr+9H0+rCli05e9QmWLLbXphhMGS+Gk5KGwQM/aQMe+jquynXwjolI06+62sAgtlKj4jhPAPmcY9dySOoqJMSH45V3Dy296ShW1RG5J6g+6icbU+oooSli9zUpSUqCe/UUgpACTgnGQAnHpgY6CurieCvzE78mvseeyrNf5bXxT+4UtHZ5qy+sMvWi2+198Cra24kqQncU7lDPhGUq5PkCenNdjs31oEF75ie2BBdzlP0R1PX6/hQiHqPUFuZVHg3mZHaUkoKW3lJG0qCiAAeMqAP1VtvUeoEZU3epydySg4kL5SeoPPSnRnp6Saf7r7C5R1F7NV6P7hyR2aa5ikJkadktrKUqKDjckKztyM8ZwT8BnpzUdlRZEGU7CltKafYWptxCuqVA4IP104c1LqF9alv3ye4VgJUVyFncByAefcKaOPOPuKedcU4tZKlKUckk+ZNTI8Tru0/iVCOVf3Gvh/2znGanun9Q9nsKGGL7aESXu7SnehlQUhWACeOFY5OPM+dQIda4d/bFfGmYNTLS3KKTvqrKy4I6hKMm1XQnT970Yt91cVhtLJWe7Co3ITnjPHXFJ/O+lOvcs/+GP6qC2XV0+ywl26PDguNuFQWXmd5UlRSVJ5OBnbjcAFAE4UOMFYfaTdoi1K+ZLC/ncEh63IV3aVd5uSk9cHvT5+ScYxzpXa+Z/oj+wj+m4v85fuKG96RGQWmf8Awp/VQK+3mA/+x7TBYbR1U73ICj8OOKMtdos1DamvuV0wsqwElVpRlIzkgY456HOeOmKC6sn3GdcN9zsEa0vAAdwxFVHHCQnO0/xPtJpWftHNmg48KS6pDMOixYpqSk2/NgYPveah9grkyns8KH4orjjnNcHrXLcmdBIV9qeznKfxB+qiVgkWuNdWpN0ZCooTlxKk7x1xwMUIpQ/tX+x/So8GRwmp1db7+QOWCnBxfjsWhc9TdkTkd1q02hbLoUS065F5UCropIyE4TwMZz54oOL5o3HDLA//AIh/q1CoEoQpseYpht8MOocLTgyle0g7SPQ4xUua19Y1OZldndldBUslQ3JWrKMAE9OFDdwkHrjFdCPbOZfoj+3+5hfZeN/rl+/+w5+etH/vLH/hD/VoJfbzAkn2e1QmG2/wne5AUr3DjgUQZ1xbRCjxn9DWJ91htLffraWFKASBkhJAJ4J5z1oDeZ0W5XFyXBtbVvZUEhEdolSUYAB5PJyeearL2llzw4HFL0W5eLQ48MuJSb9WNDIdz1H4orDId2nJH4opJYUPdXCVKJCfKufxy6mxRFu/eAJ3j8UfqrG3S4oIcAO4gZAAI99c4SeM1tpsh1HI+kP01abtbk2oI2t22szWXLxGdkREtrK2m17StWFbBnyG7bk+maPoY7PXowXCj6nfloKdySGe7xkZJI56ZwOOg554iayQEem0/pNE7I4EtyT0yUcfjUalQDjbJYqF2dh1t9tjVjPeOJ7zDLQQ2nd4sZKiRtJAB5yAcnJAYswtCSYaZNwt1/ZkJRh5uIpvZwgZWN+Scq3EjgAYrqTpnUzUtm0qsstUqU2y8w02nvCtLiSpH0cjJAVx1BSoHBSoABLhX2yXBPzrbZcFwpd7tMhlTZUEhSVY3DnCgQfeKN5Y/q5MWsUv08wikaS+6GxjTC7qr9mNJkCehtIOFIAKQgnqd5IPTjFezFwrU6CF26MsHk5aSf5q8S2dTMi9W2W0kJUmWyXEjy8Y5Fevvuhjp+k8kfXS5Km65D8TuNDHX9pssTSdwlxbdHZcbCF72m0pV9MZ5A9M1VkOWr2OO8kkZGOTU77RdTQToy6te0JKls7UjPmSKrTT6i9aYaRk5TnP11q0UqyP0Jl90tizKD1r9SU1UOvIvcXZZSMBVW3anmolsCnFY8NVbrmS1NmlTZB2g5NdTUK8bM+J+0RyFNTju14rJTDbuVJxz6ULcKm1nacViZzqTzyK5bntTNVCcxsozkeVAJf7b9dSN2S2+ghQqOzinvuDWTNXgGiQWcf3tV8TSmhbvAtN4fdmKCNyiAs+XNSLSuj3ZunhLL4Sp1JWlPuqETtPPNy3kFzHjP6ax4cqhNteBqz424xdF0DX2kSElyYycYzmnI11o3H+Gx/tFUN8wPfv35qz5jd/fR9lbfxsjI8DfgT2srXSt9a80e8MrKysqqIdeXNazWq3njFUGMb4f7zzif3hf6Kq+rPvv+Jpv8guqxAKlYFdPQv8t+v2PM9s/wB+Pp9WdttKWRt5zxxTxMdpjHfq8R/ATyTXcVtae7bYAU8908wkeZNEX2Wrc13EN79lqAU69keBPqT+D5YA55GeTiuhGPicOUldA+VEdbaS85b3WkZ4KlYJ+o1z7DKSyH/Z3ktEZ3Jwsfm6UsLq6w0lpq5SjjOQ0e7T19eqvrAp1CvEtZDaLkFrP+TmjIV7kudU5/2fjR+yDUvAF90XBlKd3vSP0jypMpUjr58/VUhfZauJcfiteyT2Dh1lfBJzjHvHTB9Tg54JDyQl5HfY2qztWk+SqtrhKT4huFg1yskrUT6mtDr1ra/pn41UnsWlQesehdRajhJnWVhiTuWtBa9pQhwbU7icLIyCArGCT4Tx0ycY7LO0u3SkutWIsSmFLKQZbAWCjG4gb84GQc9MEHODmoQYkr2X20xXfZysth7YdhWACU7umeRx7xTuzTIUS4iVdIJnM4Xub71SCVFJ2q3DnIUQffjHnVQk09mSSTW6HMi+XxAkR35zhTJWHXhkELVgEK46+Vdat1hqDWs9q56kmiXJZZTHQ4GkIPdgkgYSADjcadyLnpR1SlMaYcZSVlQT7UtRSNuAMnrg89OfzU1vs/Ts1rZZNPOQF97vK1Si74NuNmCPXnPWnSy5JRcW3RnjGHEpcG/w2I6rrWqULThSVhJKU9SBwKTrJI2rdGUr/kj/ACf9KkqUOQ2f4g/5qvH4+hJchOu2nFsuodbA3IUFDKQRkdMg8H663GkOw5LUthW11laXEHGcKByODVmWTVWqxb2XY2r9NtodbV3kSWEIUOn0jjJIwk8qyVJ5z1UKKlKMfeIMdQ3JTkl1fsoMtAbc/YjQGAMDaNuEnHmnBpC3TVW2dHnobaWqO4l1KXE7kqIOQCPMe6pvfNaau06401F1XY5yVKVtVb2WlBASUHxeAcEgY69DQhXaXrFU1VxTc0MyVM9z3rDCGzt2qTnwgeLC1eLrk56gEMi3yF8MJLbkwdqvUrmq7kLi7bYEEpaDYZhNd22ACTnGTySTQJWUkgCpgjtM1ghZKbmkpU6p8tqjtqbLitm5RSU4JPdp8vX1OQ1/1Bc9SSUS7q6hbjbaWk7GktgJTnHhSAM8nnGauSbZMUIYYqEOSBKT60qyT3qP4w/TXLQQHEl1KigKG4DqR50XucG1MlmZbJZUh5YwyoDcge/BP5/z9aZjxylFzXgVOaTS6g1xJCUHP4P85p3a3/Zip4tocCFoVsWMpVjJwR5g02e5Q2f9E/pNJofW0FBBGFYzkA0Mti0Tqb2z60lznrlPlMTZzoWPan28uICu+yEhJCQP2S/gY/D9wxHrtq+56jDMaa2w21H751KWkqHiLKUZJUSfoNNp/wBnPUknuwo0e9EW5qC7TI0oOEJaZhoWlSMDB3E5zuJ4x0HXmg02Q0mU+3BUlbAWtLThaCFKRkgEgdCR5e+lNjUjiC843LaU04Uq3pwRx50X+cb+4QHLvJ/LK/XQm1JC7nEQUbwp9A2/uvEOKml1hRI2o7gY0MtxospaGGEr3FQQsAglX89LlOgoxTAX99pKghya5IRnJQpxRyBU50xeZjLDLzFvSqM3lCm0vbinHUkHkcfooHMuDLrTMmOlI71ZICQnKQU/R4AoaxNeVMg+zJKHAcgp8/X81TFllGScdhrhHgZdN/urzEHDS8Ap4qvpErvVkKOc9akmopLjkFjd1UhJP2VD5JKTkV6PUSZhxxSEJjQT4hQ9Z8qeLeKwUK601W0sEqxxXOmrdoehs6SQcUJlcLyaLOK6poTMzvrJlLLC01rYW6wiCh5sbUkAKPiFReZd5D0hbm8eI56UxtcdLgKia7fQlLm0euKyLGk2+poeWTSXQc+2PnkEfZWe1v8Ar+akEcilAkkAjzpJuTTRMeepGPdnOK2Ca1WVzj0p1urQrVZUId1maysoWXYxvfNnmj/UL/RVaoBGSB14H11ZV65tMwdPvKv0VW7QAOFdApJz6c11tCvy36nmu23+fH0+rCtvkMW9t2a62Vnd3LQAGOBnnPlnbkemaSEd+4yzDRIQ2lILsp99W1KT+GtZ5PBOABkk8AEqwUXwRa0Ak/e5DiVAnjJCf1GnkeG9dr6LKgqQmdLBISDvcBOUBIPBJB8I4yVDmts5OkjiQW7ZKtO6r7I9LPpandmsrV6AUlyTPui4e71DbLQIQP4ylk+7oPRF2+Tf2R9uHY492o9gMKTZrpAadU9annluIeW0ApxhQUVFDu3lBSdqtycgbspqOx6O7DtJWhq59rCdQxbxuJVp32lK3wnPhKwhtCm8jnC1JOMdc1YXZtYb72qQZUfsI7I9L6Z08mUppd61I8q4q7xISSUsPd42FlJTn72vHA3A0psYlZ5ngz3FR0zSrL9vKQTuILrCjtKSfQEgfBWPKub4017UpcdRU2+kuBRGMlJPP1jn666vl3uN0u9zlzruu5EBbYfxtStvvAE7UjhKM4ISMAeVNVnZCiBWeW3ldfI5H8xp0Zey0KlH2kxmcggjz5rhxZDih6GsWcFPrtFcu/tivjQ3sGo7khseuNT6ehGBZroY7CnO9KO6bVlWUnJ3JJ6pH/omkJ0q96kuL12nBT8iUtTjjuwICiSSegA6k1mlzYA84L7LkR0jBbWy2lR888FJ92OnocZyCVsuVofu8pm6XiY1b0BXs7yEJ3qAVwCNh5KSfTnjgHIJNcNrmU07p8hoxZHSQJDqUjGcJ5NPW7VCZKD3ZcOeq+fzdK5h3GyKvUtq4XSem2pDnsrzTSC4cHwbgR5+fp191KW2fZHrs+i53WWxbkn7y42gKdUN4HTbg+EqPOOn1EH3k/Ei4I+Ale0hNrdSlIAyngDH4QqKKHNHb7NjuS3o1smvPwcp7tTzaUrVwCcgf6WfqAoKQAakYOK3Lc03sJkYpTq3/sf065UMnHupQJHdH+T/AKVFFVZG9hEKx5A/EV2HlD8BH4orI5ZRIbXJaU40lYK0JVtKk55APlmpW/eOzdfcdzo64ICMl1PzgcrOcgZweBnHAB4JzyNo7oKkRUPqH4CPxBXYfPmlH4oqUJvXZ81C7o6MkuPKjhoumcRhf74OOvmfI9MCuvnvsyelOvv6KuLLairu2o9zwlIwoJ+kkkn6JJz1zxjipxNFOKZGA8fJDf4oroOKxnYj8UU4tsi3C8xX5UdIhh5tTzYKsbeNw6k46+dHe06+af1Dra43XSsNMW1vLSIzKWktpQkJAwEp4A4+J6nnNOi7V2ZJZGsyxcLqm78Oa29X9CMKUTztT9lcoJLqOPwh0Hvrpp0tuocwk7FBWCMg48iPSil0m22f3MiNFDMlSwXQjIR9Q+ymxxqUXK6a8OocpOLUa5gwnLaUH04+00XsEt+FAmuxpLrDheZTubWUKIw5kZHlwPsoO70R5cfz0b0vc27R/fN+3Rp6I02M4qNJBLTwAcO1YBBIPngilUm0m6CbaTcVZL+zrSHaF2pX9Fi0xPnHHikSlyXAzGb/AHa1A8e4dTQy4sTIGs7tpxGpZF9tkKPPYMressPrRFdUCAokcKTkfxQa9IdgvygtE3xM3QM3Sti0lLuYUIioiFIiyFqTtCHOdwV0x4uegwetYa51y2piV2WXDQlu03d7HLub7ybYhSY7yTAeTvO5RVu+iQeQUq4x59TLotNHSd5Gdvrv+3l8Tg6btHWZNdLDkxUklta5P9V+PTbl4lBW59ce4RpDZ8bbqFpJ9QcijcnUM+RcplzBShc11TziAMp3KOTigcMB2cwnATucSn89SgaYZx47qgHzwyT/AD1weGz03Iaru6ZSkhxHdAHJ5ynpjAHlXMEOzJsZuG2pS2vGrHHA5Jz8BT77mY3P99f/AJP9tLW2xQ2Jja3Li6Ru2kITtJB465qLHuFxuqLFu7XewI5Hi+9J59eBUdlW9xTRUlsk4qRCcw8nuccIGBSiXI6WVFSRj3128+qgLhhdFduMPJXnYRSyMLbIUOffRme7EU4QlI60DnyG46gQcZrDHPG9w3jpWMJLRQs5FB5Yyqji50d1B3HmgspaCslNKzOLWxSQrAf7tJBPOKTeeUpzJ9aUhR1vcgcUo/GCDWcLodMnwAnzpWkmhhOM9K7yayvmdGHuomnTjNZWhya2Olc49OZWVlZUIbB9a3nitVmcVVEGV5/xTM/kVfoquRhLoKkgg84Pn51Y15/xTM/kVVWxOUg/VXV0P9t+p5ntt/nx9PqwvHMcvll9IUxJKVDJwNw5Az5Z5GfLNK3O3goQwpZDrIIjOKBHftg/QPotJ4x9XpkXFfaKfZ5B8CvP9z76Ixbt3SDEuLYlRgQFKxu6dNwyPhkEHHGa3pqSpnFacXaAbm8LUHAQoEhQPXPvozpbVer9LyXl6SvtxtzklpbT4ivqQHGykhQWAcEYJ69OtO20WOQjw3hbQH0UOIQ9j6nNu37TSe6yx0IM24ql7TnuGvA37gUp4OR1O4EZ8+tBwWEsi6A5qI6ttMZgEtle55/8DI8s+gBPxJ+FJzX0PuqLQw3gNNA9difP6/10+mXF+chLJbEWE3gBAASVAfDHxx688nmhUt5K1Du0hKQNoHmBVNpKohJNu2IrUCokGnYgzXEh1NslKSsZCghWCPd4aY1YNj7WV2G2MWdWmoM5ljJxJUSCspQkKxjyCDj+Orrmn6THhm338+FelidRLLBLuo8T9aIaLbOPW1TB/uz/AFa6Fsmn/sub+TV+qrStPaZfZ1sRKt/ZVDuDUULCpRaW8EpSEnGcfg4J+BPQChM/VGpZKmUr0RLb7hpuPhJWceLYColJwVKyOcc8DAwK3LB2ev8A3f6WZe913/y/1Igotk4dbVN/Jq/VXZt8ptJUu3ykpHUqQQB+apmi5aqcUpCNC3NRQlaztbcUNqeVHIR0AIyfeKCaia1jOS4qbp25Q4rG0rQuK4kJJxjcSkcncMfEetDlho8cLxZHJ9OGv5Cxy1U5VPHS63YE9nz/AJB3/wBfVSao2T/g7v2/2UiCRxTmPb58xJVEgyH0pOCW2lKA+wVh4kzTTXIXjeyMpKX7Ot9ROQouqTj3YApCS7HeKlRoojoCAnYFlX4Wc5PxriVBnQ8GXCfY3Hwl1tSc/DPWuFLKkKUrklvk+vio+9uPCv4X81ZXBTt/U5jhj2hr2oLLO9PebMbtmecZ88VKn/7lZUyWW9SpSUqLwJZyDzgJ4xjkDJ9OnNRaNvcebZD4a7xQTvUohKcnGSR5VKEaTSVtJ+72zISpsuOFUxX3r0ScAgqIycJz05xkZTs+Y5WdsPdlqIkhMiLfXX1MAMnKAlLuwgk4PI3YV+bHXPAX2UPOOOOxNTRgorKGWnmVpTyraNyk5PARn4q9MlReiZaYgl/d3p5QIThtF0KnCVY42YznkZ8hnrwcCb5aJdjU2k6giTe9SFD2SUpzb168DB9R5HjyNVSJbGVrED51iCZu9kLyO+GRnZkbuenTNTPV83suma8vEiA1PRYVrSmCmE0hspTtwSAr0IBGeVDOcE5qDe0Sv4U9+OawyZXX2t4f7w0f6aRmeFPMs1vZNVe29O667EtaPZI254zqd5tbDqTu7hKm3fD3ahjgjlWR7h6kAfeTogx4o0yLz7VvR3/tpb2bcc7dnOc46+Vc2/T/AM5Q40tzV9sjGQpxBZkSVJcb2gnKxjgHAAPQ7h6HA64W022THQLlDmB5tt7dFe7wJ3DOxXAIUOhB6Gqj7yofK6Gzg4T8P5zUt01pOPc7I7Lf1DZ2m15fdZcn9y8wG1FAKwW1DCivj6ufKoko5Qg+g/nojZmYryXlSY6Xdm3buUoAZznoR6UctxcdiQQ9F6cmRGZ7eqbSwpwftT14DbqF7QopI7jjrjPTIIzmlY2nLfFU7dIWp7Gt5bDjAVJvO44ebU2rwd0lRKUqJ9OB16FS3dn13vekr9ra06bjvWbTSoybnIMgpLBfWUNYQXNysqGPCDjzxUKeVGcdUy1EaQEpV4klWcgE+ZPpSWnVWNVPejDbhbNRotrk2LJDEpLZfjOBxpY3DxJUPpD31dbmlNJoaaeD29RiqcWj2tOA7sbKUYyDklS/dx61REPPtjBSMnvE4H10fLl46iAftqk6D5lmM6d0uyhl2dMbwHHkSUJlAltBGW1J/dbfPHWo9qVm0wLrFRZnUusFhhe4L3blkZVn0OfKooVXgjPsWK6jC6mS0p2MUoSsEkAcVHLoRKuZO9NoW88S8c55NSOfGZMZQR1xQnSbAlvY4HPkc1NJdh2RisA9KxSyO9zqQhFrYqZ9ktvqCj50GvEZT7g2qx9dSq8xC1LUAKj04EOYNXxsvuoy2AwgED6RP10k/EDaaJnrzTaX+1mrjJ2DkwQjG0K2p1KGig4BrUsblFQI5oaiQtvhNdKlOr4JrQjBtyHKD611x6muGuUgmuirnyrO1ubISSirJqK66VzW91cw9Sjdc5wa3mtCoWdVla59a1k1CDS8n+9Mz+RV+iq3T4kKSOo5qybrtNsl959EsqB+yq7aj/s9uM66loOLSkuKztSCcbj54866uiXsN+f0R5ntrfPFeX1GxPQg812pzJSvbtJA5ScHjj+apUdD2kJZcVri1IDziUKSo5W3k4JUEkjjgnCiPf64nQ0BZdaOu9PAM93tWXl4WVgHA8OfDyCcYz7skPbOXwsiinPVST7ygVrvSOjmP4qQKkg0daO/Lb2u7MGwls940HVjxKWkjG0Hw7Ek+5YNLq0DARIajq13YVd+gqQtDxKU4IHjOPCDnrzz1wApSRsJRIiV4GEZGepzzXPSpj9wFuWpSI2vbE4UrQnKnCgbTnKuR0GOgyeelC9R6ah2JAci6otl1CnltARFKKto6LII4B+P66slAKse+maytvDxmrfIrxHkK/3u3xhDhXSSxHC+87tDhCd2Qc4+KUn6qNWPVd7cmCJc9YXODCfWXH3ULWs7wCUkpHJ8WPtzRjREy8nSt0EZFjeiwG3V93Pi96tJWkFSm/8ASwgDPOPd1qTN3vUTUSxsybTpCULqy2w0qTDWtbaUIBTvVu6+eOmT0AxjRDR55xU4xtP71/Jmya3Twk4SnTW3yv8AjcheoNYXuPcO7s+vbpcmNifv6luN8gngJVg8cc488e6hUjVuppkNNulX2a9FAIDLjylIwVJUeDx9JCT8UijsvQs2fdb0Fzrcyq1pS46GgpKFZSVYQPqPTj04oXdNJSLTYIN/cmx3G55AQ0jO9ORnnyFNloc8E5SjSV/J0/mKjrtPJqEZ23XzVr5bgJKfOp92b63g6SZlomPLSJAUnalsrzlTZ5+pBHr0qB1rdWdpNUzQnTsmPaHquDqh6O5DkPOBGCrvUYUCEJT5AA9PICokUAtHHXZ/SpPOTilk/tR/if0qLHFK0ipu9xsE4qSsaHvr+ipWvQylNqiy2oW8qG5biwo8DqEjbjJ6k4GcHEex7qcquU5VuRaFSnPY23lSEs5wnvVJSkqx5nCUj6qOHCr4l/2DPjlXC/Hf0G9ZSiGm1oyp9KD6FJP6BWFhkdZzf4i/1UNhCdayPWlmo8UuBLtxQlJyCoNrOPfjFGbJ9xyIspm/GQ46vHcPMIUCnkeR49RyD1q8ce8lw2l6sCcuCPFTfohmnTl1csyb9HYDkUrcQspPLewAkkHyIJxjPQ0MQfviMn8IfpqwZGqdGxNMSNO2V2dscLhQp9vJypsp5I9/uqvTtS8gJWFjcOQCPP307U48WFwWOV7K/UVpsmTMpOca329DpSsBH8X+c08trpQ2/wA8Eo/pUNcUQlHwP6TUjtejdWzLMi9Q7DKegyiotvtpCkkNkpUTzkAE4yeKyOZrUD0v8l/tD0zoDRN5d1ExBskTVbn3Px7mvdIkTZpGSopPgbjsJdQT4cEu8leAE+adatvsa3vEWXZ49qlMvvNSYkfHctvpSUu92AAEoKwpSUgYSkgDOMl3M0b2iy4zVtlWS7uRbap3umwxubZUTlw8HAJ2gknySPICuZGj9fXZ0XyVabpPVIjpUJBbCt6O6SEEqBP4BR15I+ugsOtqIvAO2dHPo6g/nFTE3U5PTmogI0iDdExJbRaeZdCVoPVJBoj3+fOhJyDS7kpYISvGfMU1dmugABxRPmaH9+cZzXKnyeQahexY/Z5JX3yVKUTkAn45NXQrY5D5wSU1520ffG7aXVuHrtIHpyauDSupRfY+G+g4rJnVOzoaaScUgHfLOXZbjoTxzUAusGSmUpKWVq+Aq6psVG8JODuOKcNaXt620rWhJUepxSFko28NHn82+aOTGX9lNpUWQEne0ofEV6NOl7ZjBbT9gpvJ0bZXUnc0jn3USy+QM4WqPMSh997sDkmi0exrcbDhJq3NQ6C08xGW+lhsLHIIFV+CpAUyz9FJIFae8417Jg7pY5e2MWbNuG3PNaNjXk8GiEcvsr++DjNL9+r0FDwyGd5ja3Q6rK11rdc09OjKysrKhZg61lZWVCxlezizzT/qFfoqEWR0O3KEovrYcjyG1h1CN6kpCgSQk5yR1Axz091Ta+cWeb7mFfoqAWQuJvUEth8rElraGDhwneOEf6Xp766eilUGn/zY832yrzRrp9WXFOuzkq2ohOdqU5cd5xXeEaYUhLbRZytRwjKipRQjryDk8GmrGqLLtduDPbEWpTjiVSHH9OoUVkHaNg2k/QBPocpB29QTff1E3aHn242tW2SsLfWlbCm2Y2dqykgYBLRSM5x4iD4Sd0buXZ9YmogfTpDVbI7lTi3y5HWAtexTYIB4TtUPQ5IHWtDVczmRdjtWtYLTKW2u1557vEMqeH3PJ3btxJQlZTlISDx1G7kUsdXWiPJiT2u15syYqHkJWjTRB2LCMtkKTswSFeLaT4lZJ4FDH+zS1uSvZYGmNUplhSHTFdejE90lwB8JwQVEJUAnplQVnGCK5HZ/Y56VvWnSur3QHkqPijlIZSsh1II6qASvb7wD0NVRe49ia2jSXGRO7Xlxmu6C3e708kHvO7DZThKcKOxaxuPpnnioTrdFgdki52zVovcya4tyUU24xEtnyITgDnnpSwi9n1udkW++wtSNTI0lbbiUKZ8IStY2kEfSxsB56g4pldVaAVagLK1fU3LKce0raLOPws4G4+7p+uIpsj1SGJo/VFzjNzIWmH5LLmEpcbSpQUrGccHrjBxUe6eVTa1dqeqtPQmrdanI7bKE+beScpCTk589qfdwK26SOnbb1DaXkZNTLOku4Sb8wSjSWpw26tGm3tjJAdUCdqCTgBR3cc8c1tnTGpHl7GNPuuKAKglCiSABknhXQAZo8z2udoCnjNYWtanT3e5KVqCiAfD1xxvJA8s1uT2va7musuy+5eWgnuwpgkcqB4HT6QHl5Vt4ezf85fsvsZHPtB/oj+7+4BTpnUh6WBw5z0WecdfwqSk2u5xGe9l2pLbaTjK3ePh9OpPL7X+0LuzHmJQlLrRTtWwRlsgdPdgCoddbzOu7wcmKCdv0W0ghKfqNBleijB91KTfol9AsS1kpfmxil5W/qc5R/Bmfyh/rVvDfnGZ/KH+tSDSHHVhppClrV0SkZJpV6LKjFIkR3GioZAWkpz9tY1NPwNTQ7jzERm9ibZb3MnO53xH86qRlPqkqWTFZYKUBOxpG0fS9Prprj4UvyWTk5Pd9T/GpqySa4fAW4JOzURLTkplqS+WWVuJS44BnYknlWPPA5xUo+YNFuRBLTr9TKlkkRV291TqB6KUMJPpkHr7uaiQBzU2HZ4mV2eI1zAv0dx1reZdvUjatpKXdmUqzhXBQSMDAUnk5ApdWTJmhirjfN18RFGm9DriMyVdpPcuuIG+Obc8tTa9qequARnd7xx9LmoveIzEG4PxIN09uZacWlL6U7QsBRAUBk9QAevnTcnnFcq9aBp9RykJ73ME71dPWtd67++K+2ulgbT9Q/PSdKlaDW4Raj25yIh167rbeUcLb7tR2jcBnPnwSce6mLiENzChl/vkIdwlwAgLGeuDzzXIPr51pKcOoB/dD9NAk7uyJVe5jmdiMnyP6TReyyJzsZ2MLrMZYbASGmnilJyVHp06/poQv6DY/0T+k1Nex2babfrm1S757J7E1MaW77WElnACsb94KcZx1BHrUGJApRW00GG7lcA2joj2jCR18se8/bTVd0njbEZu9wCW0lSAZBKU7U8ADy4GK9S3yz6ofmxNQ3jR0DTtmnOONfNqmLc+xICW20IaDyUB5KnFlRWsAEbvvYzgJr/5T9s0tYrzYdM6PTDZtdkt70RpMZlK0vSCVqkqEncpbgS54NjqitBB8jky7LoowzJlxuvt02S7JlSH+8ddcUVLcWTkkk8kk0R+b7h5w3/yZodaTi6Qyf39v/mFXvYZEBN0aVM7oNAKz3gG36J9ffWrBgWaLbdUDIpkW24qISmFIJJwB3Z5rlVvnJB3w3khPUlBGKv25S7UpJDDkRTntKC33e0eDHPA9+ajV5MaalcaPsKnGijw+ajmmfhY1dlJW6KtgpIKWiQCtQGavPQmnG7fa0yG3zlQ3VWETQt29pbUtICUqBNXRYo/s9uQytZGBXB1OeNUmdXSaeadyiNpUtYdB3ZwaeNXScWwA2AkeZrox4aF71BJ5pV+fGDfdNpBPuFYHmSVpnTjhlLZoSVcpihwcU3XJmufSdVXBfUeBXJdcPQUr8VMetJBEX1jKmdyWUrUd3HJqLW+FJJzt5NSbUFvucx8KYTwPUUMbtN+ZOUIH2V08GeHdq3ucnU6efevhi6G0iFICuUZ+FJ+yPfvJp+qDqFasFpJx7q383ag/eE/b/ZWhamHVGZ6ad+6xgK3Wgc1uuaenRlZ51lZULMOPKsrKyoEMr3zZpw/1Cv0VWbTrrDqH2XFNuNqC0LScKSoHIII6GrNvAJtMwJTuJZUAB58VXC4EoAKTGeIP+ga6OkXsP1+h5zth/nR9PqxaPdpjbuXpLriFcLClk5B6/o/MK7uL01tYKJzy2F4W2d5x5fYRgD6qaCHM8oj35M0QhNPPx1QZUV4IzuQotnwH1+HTNboVJcD+BxZ3F8aGQuVxSreJ8gK8XPeq8+vn54FaRPmtjCJjyQc5AcI69f0mtv22dHdU05Eeyn0QSDSfskv+CP8A5NX6qW1WwznuHdIaXma5vqLQzPaZkPlIDr5JBUpaUDJGT1V191SrtS7C772VHF3vttn7mUvoXC7woWFL2Y8aUkEHrkVC7Bcr1p2em5WyO6iQjBQpTJO0hQUDj4gdaN6v7Q9d67QBqd32pSWw0hSIaGdqQrdjDaUg8+ZGaovZcyHYwDSrvKQf3PH/AK/PW/Y5f8Fe/ENOWWnlHY5bVHcME7V9ftpkKewuXUmGgGu0FdreVpK/wYjK+8S40480lxITtUCQoEhKiClJPhyFg4zyZQrtdWhYZ1fbXJB3IcYTIjqd7vuw8FbtuFJIcVjCjyFDA86zVGfB2m1K/Fc/XWu4fHHzWr8Vz9dVXmEmWe1C7b7bmH8+NpWQtgNuyo7qwlHBSkKyQkAjpgBODwkZEdk9nerb3LRcHLlaJbtwZTNU6JzScqcOdqgcYVuVjptyetRMMP5ybWr8Vz9ddBl3/Navsc/XVqPmSx9pdm5qvsdi1zWYctQVsde+gkFBJzwc5GR0PWnWr03VuYyLtqKPd3ilzxsurWGvvq9ySVJGPHuPHGD8KEBh7/NSvxXP11sMvedrX7vC5+umL1EShcuJDfPOKWz95P8AE/pU8jPSIyCgWJhzJzl2OpR+0muX/bJLnefNIbG3bsbYUlPXPl506PCo3e4L4m6rYYV2mW+22tpDy0tuY3pCiArHPI8+aX9mlZ4tavya/wBdYYss/wDZKz/u3P10HxC580ItqiKTl5xxKvRKAR9uRWK9hPR98/7of1qV9jlnrZ3Me5tz9dZ7HL8rM7+Sc/XVXZfDQ3PsJBBff/Ij+tXO2D/CH/yI/rU6ECcc5sruf5Jz9dcGBPBx8yu/knKW0Er8BuUwR/7Q/wDkR/WrgFpTqEtKUoBSSCpO09fiadiDPBBFjdOPVlw06U/eSkp+59pORgEW/BH5quCj+p18C3xeCv8A56Adf0W/4v8AOaXhqwlxAI3KKcAkDPX9dKO2q5kIItsr6PTuVep91K21u/2mczcYECS3IYVvbUqLv2n1wpJH5qVyGJisK+aniOvG13ydGL7ZZc7iaWy42SCUqwoZGQODxxSZuN0MBq2S5m6Ew4/IabK0qCXXEBK1Dz8WxAPl4RUje192nSC0px14qYdLzShbGk7VFO08BsA5B8weeRgkkjb/AKj1zqWK3DvypElppwuoBhIQQolRPKUA9VqOM4yaosDWZKVXeElYyDIbB/GFXt8yWLg9yz9e41R9nhyk3eF3sZ5CfaG9yi2eBuHPSrYXcwFFIVnBxn1pOZy24WYNdxXFok9o07p2ZM7h1hkjaSNiOc/WKRTDtcC4BEWO0Fpc7sKQ2ATzihdiu5ZuSF99swDyFAeXvyKYXK8Fi4LfdJJRI3KAOcYVmkyUpQoRh4lKMvMsZNvCTnaPsp4hKUowaruT2tWxOe7dB+FMHO1UOk9yhZ9+K4sdJn50fQXrdPyssxbLROSoVx3UdP0lAVVMjtJuCs92yv7aGyO0C+OcISR9dMjoMst2BLtDFHlZcpXCT1cT9tJLmwUDlxPFU2NWXh1O5buM0xlXu9Ok7ZK/iDTI9nybpsCfaMYxui6Hb1bG8kuJNNXdS2xAOFI+0VQ0i7XRS9q5jv41KMTJJGXHlq+Jp67MS3bMy7VcnSiepND3fSc2OVy3Yu/oQ4odalBe0Rn9sg/jJrx4l93GUuqHwNZ7TI/fXPxjRrSRW1gPUuTsmA613XFbzxSWjtI3WhkHBNYTmtCqLs7rKysqBWITkpXDfQuUIyVNqSp5QJDYI5UdvPHXjn0pXtHu3Z4xaG2dKPMPSl3ABqTCefStMBLZH35K17S6pRTyEgDaepUcNbzxaJx//buf8pqsAc10NJ7r9Tz3a391en1JnIvGi+7aMSRqYLQ8lTuX0gONd54kjklKg3nB81HoMZLyJcOzZ1HtEu+ayjKwgqYbU05hR3bglRI3D6HXBxn6XkG0DL0xDub6tUWdy4sLZw20gKyFZBzkHjjPkfqqVvXrse9mZab0VcUPGYHDvUSVM+0E7fp/vXg8+R59a2qPjZ5/LqZY58Pdt+ar7gK7TtLO21XzDe7+iYlSEtomrSO8BBKlZRwANvQnOXB5JJIK0XFartHZvd1mswy6kSFoWVLQ3nxFIKgCoDJAJGTViM3zscTcJaXdC3XuVx2g2j8JBHeFagCrzSUen0T8aiDkzTKtNCA7a1i690oB9bKgpTgdJCgvvMBOzw42dQfPmrkujAxapye+Nrlzrx+Ph4iVuuMM3GU1c7pLTEG4RnGpBCj4gAo5z+Dk4wPiK6uE+3Nw3VW2+TXn8N92l5wt5O5YXwFEY2hBHPVR64ptORopuzhMaLfUXXu0kqdLfcbsJ3cDxYznB+FR0HFL5bGuPt+1uiTLnsm3xVRrrMXNUw8ZjalnY2ob9uw7iT4QknI4PQnOAa1vc9EsWPSbGjLjeHLgm3d7fzIcGDLW4o4QsHkbNnhxhPTKlFRqFQOXHP5B3/kNSZvUGghCZYe0N3rzUdQW8mY62px0p2gnxEAJzu4HJA4AJAtDKTC8WZ2WznmGXL1rOClbiUuPLU0tCE7gMnblWMZJwCQegVUPvVwZRcXU2K6XR2CNvdKlqCXSdo3ZCVEfS3Y56Yo6Lz2dYS83pCaVofbW4n20paU0HMqSE8qGUcfTJGep60tAu/ZeiClq66MuDjymglb7M45S4B9IJOBgk9PQD14Kr3AVJ0Q/5zuPT26R+UV+ut/Odxz/AIfI/Kq/XR1y46H+e57sO0zWLW9AcZjtO7XnUyCjCFk7k7QF4PBPHGDmlrFedFxIDce7Wx2Qe6dS8kREFTjqgrYsPd4FICSUeEDB2HP0jigMk3C6i2Rz5xnnkzpB/wB6r9dOIM6ap1YVMfI7l08uHqEKqcL1N2OLu8SXI0nc3okZSlKZSlpku5UnAXsICkgBWANpyrlRGAIU3815WYKpO8pkja6E4Dew7TkfhHPIxgY884FtV4i8OaWR+1Bx9Se6mXpaH2I6Mk22+QJep7jPuDlzaYdUJcJlte1pDwxyFg70nIwAOuTtDQZvZ1JTHTPueq4rhYSXy2pstpdSnKgnJKiFEEJ6YyM5GcDI950ei1MQpGlSqUkJL0sSHNyilROAncEgKHhJxwDwMpyY0ScnHnQmiidrndlyY5Dd11up4OqwSI4SpvadvG7g7tueTwD7hUXuc9CbjJFmuNwXBDqvZ1SVbXS3nw7wkkBWOuDihqSB1qep1P2fJnF75mWY2Vdy0be1uZQeiCrvMOnp41DPB4yrKbSsTlySx8ot+hCvnCees1/8oa0bjPHSa/8AlFfrqZT9R9nz9ruEWNYXm35ClmMv2VsFrKwQSrdngeQwnyx1JGXW76Tk21iPFt7odQ8yo7I7bKkMhCg4jvApRcKiUEKUkY2+8ir4UvECGaU+cGgPBuE1b5SqW+R3bh5cJ/ANXtbZnY8jshSmdIt921Lb7M89hEgQVJkPoeSkLUpXeyVtLLR2pB8SRwGipVUWr5u+dnvmoP8Asndud135BXjuznOOOuaJWe8aQiWoxLvpf22U4spMkPOIU2gk+JICwFH6OEkY8J5O7wizUhRu46QVZ3BIl6lauiI6Q0W3G1MOP4WVFeSFJRnYABk43H0FR35yuP8ADpH5Q0d1Dd9FXGChNi0vJtkvKN6vbC60AB4gArnknOc+Qxjmo0aH0IOk3G4YIE6Rn+VP665+cbh/D5H5VX66OQZ+izbbLHutvnKejSZKrgqKENqeZUEd0lKyTkpUF5JSOF4zwKf3DU2j2rrPdsWmWm7a/FCY8aUwlxTb5bSgnvCorCQdyxySVAZ4JwVWjO80uLhUH49K2dfPmvIinzlcf4fI/Kq/XS8K4TlyAlc2QQUq4Liv3JqYalvfZjenbfHtlmnWyK284X1tsNJcDRT4E5BJXhQGVKycZ6niozMTp5M5n7n1TyjY53glhGc4Vgjb5EY49c9ajjT5hYcrypOUXF9GFIVqaltwblbbw6e8faZVGecT3hcKwk9FdOh6dM/CrVT2PazX1jBPxfR/MTVIaYO3UdrV6TGT/wAYr2RGuRTNlbl8KVkcq/nOPsoowUuYeSKlzKxt/ZDrCLJEj7ynAI/wgA8j3Cmk/sX1epl91L0MkpUrHfn7Po1aE3VbEUyQVt5jjJG45PAxgY9+K1B1Ei5QGpgHdh0E7SrOOcdaLu4gLGkeQHGXGJKmHk7VtqKFj0I61IYbDaWUq28kULviQjUM9HGEy3Rx/HNGYp/Y4PurLm2OpoUpSbYm4MK6YrjA64rtz6Rrg9KUbJczn3USisoMdSiPKhtFY3+CE+eKgnL7pE5je6YoD1p2lkBoDHOK0tgmWtW09acltYGAk/CmyfgZsMLTY2aVg7aVpFxCkOZ2kClQBjrVBLoTPPvrdcjmtjpXOPSm6ysrBzVUWd1qsrD0oQhnef8AE87/ALu5/wApqr0kDirPvJ/vPO/7u5/ymo1HkdlxjIM2BqEyAlIV3DjSEE4OT4io8eH488J6V0NJtB+v0OB2r/dXp9WBLRebjYpnt9rfDT+xTe4tpWNpGCMKBH5qdy9XagnEmXP70qKCrLSPFsWpac8c4UtX246ACikGR2VPpT85W/UUdaW0hXs77S0qUByrxJBGTzjyzjPGabXRzs9Xb3vmVi/NzQhHdGUtpTZVuG7O0Z6FWPgPhWuzkPHFu2hr91t/9tfuSLgfaZI2uuBCQduwowABgDaT0Hv61I7uzr2yWLT3aY7doyo9+alxorrS2i633b6w6243jKcqJUDjBC+DwQIGCR0pVLqlI7krVtJztzxn1xVoB44dA5fdcal1W0Gr7c/ailBQjLSE7UkoJA2pHm2mo4UlJwRUhtjujmmUC9Qrit1ON/cKSArxckEnjw+WOvxyFbfJ0GHV/O8C7PN902lvuHUNnvAnxkk5wCrBA+NW1fMCLWJVCO3lQDtx++u5/eHf+Q1NI+m9PC1sSZWj9ZEracX7Q3G2tLUG9wCSQcgfTKs/RHTnIiLaYwuEowA77P3b/dd4Bv27DjOMjNWAxcLA1Zo7jGpdUsONRFJcVGG5kLWlIDafvSMLKzlRzgp3ALUSCod0aE00DE2TSKENsCxax9uXIbacaMZCUKBewUJHKgsp4AOfF608smntCO25tV2seuUulkF2QxFbVHQ4fwuQTsznnrgcDPFLKv8AYW2mY6O0i8CIiY0owRHJaQ2mQDuC9gBKUjek90OgG0fRp/p7U2n2bYyD2r322yG4oaQ0YhcabITjuiMEFBB2jyGF54PNpsppFfatt9mg3p1jTyLmmClKdnzi2lDxVjkkJ4wTyOhximFobhPXJlq4MynWFkpUiKAXVHB2hOevOPqo1rm5Mz9SvyY+qJGoGy22n219gslWE/RCD0A6DpQ6yRorl0YXJclNsJXuUuMjc4gj6JA/jY8qurVoXKVWmGZ1o001a4zrFl1MiQ4prc69HSllYO3ds9Sd3h+KfXgVCbgtu3hC4b6VtsOiOHFYU11B3jzO3I+NSe4S4KLMyyzqS+KUHY4U2lpSWCnDaipPgTkhW/bn0GM5JMVYKVSrqpC3VpLLxCnvpkc8q9/rQsHDe9hyCrse9jaFyb1b7UWgHSwY+wOYGSkK5IznrjjFcrPZEpCAn7rEKS34sCOdygV+/jI2fDxcHioZWUNj7Hl1Va/nGQbIJIgbz3AklJd2eW7bxn4cU1yK5recVadFCscsF9v2kqDW8d5sGVbc84BIyce8UenOaDDjZt0e8KQXk953ym04axzjBOVA49AfdUc3GsyTUsFxt3Y/j9wbg8Yu4NbHu73fS27VYz78VK2LDpVdoiGVYNUsSpSPDNWxiLuKW8EAAqUnctRJTyElGEkmodbv8JP8k7/yGrOt1ztsax25CO0K/wBocW0hTjZiLdaUQWPoYQnBG0+a+G2/F5VGMSI5cNP2Nu0GNadOahl3NtC1OS+5UmMUpUhO9CSnftyTyrGCUjGTxEX4kmKUolRnWVKBKQ4gpJAUUkjPXxJUPiCPKraiXu3pCFK7VtSjZEmBLjVvcAaJW2MYxyhaQR1GClOSOlV9q/2MzWH4d1l3EvNuuOvyUKQpSjId5AUkYyMKIBUNyleLqBEU1sAOlHtNW+3XBx5M+2XeYEJ3gW9rvCkBKs7h5c7TnyAVQE9ak+i3ITcqQZs+5xAqOtKVwU5JJB8KsJPhPw6Zq4israjsORZ9ONXSexPsmpo7LSGTGZ7pKX8lQCg4FJOMjO0j08+lR8oZbuzrcZp5ttCnAlD2O8SMHhWAOfqFS8TLbGus9uLq+9Q2ERmGmlLilbrqQ4PB0TtIHi6ddwyeqoetaDeHlNPuOp3OYW6nC1DB6jyNWwMNtu/qZYyG7zBWegktH/iFX2NUW1Mhx0qSNwwVev568/W9xSZrC04ylxJHGfOpQ1q67NFRKIi8jHijI456jAFZNTpVqK3o148rx35lpP6ptKlFSlgnjzrTGqYLLYQy2ooHIwCRVYv63v7u5KJDTCVeTLKU46dOPd+mkVax1MvAN9lgDyDhA/NSF2dGuFydDPxUqoOXns+uEx750tBU+5JWXXGFDBBVycH66aOQJttV7HPjOMPJAyhacGkIOv8AVtvXvbvL684BS6d4x9dTG4XdeshDv0plLa1RwyQBxlCiCftrdnUWk4qg9FOptEQTDfeV4GzT1qwvrAK+KPoZaaHhSBWnJTbYOSKzWdClzYLRp9tP0uaetW5hpGwkYpvIu6E/RIodIvQ5wqrQuTgFfYbchW4pGa5Wm2pOClNRt68FROFH7aZuXIk8ro0mJeaK5EoeRa158KaQ7m2egqMquJx9M1z7cf3yr4WK7+PQmwPHWuhnPupPpXYrnHpkdeVaFZ086kHZ5pCV2ha905oOE+WH9Q3WLa0Pd33ndF51Le/bkbtu7OMjp1FTmRulbAINb8qvm/8AyRtWW35U6fku2i+M3Ce7IaDN2cjlhpUVUYSVvlvcojY3vykKJJQQDkij/aF8kLSlp0k1rnsq7bIetrPB1LH0pfVC0rhu2+W4tKO8SlSyHW9yk9CM5GCoZKa4GL/EY9t+Z5buqFu2ma22hSlKjrASBkk7TVb/ADRdf83SfyRr3ddPkVm2dqna/wBmn90oOHsp0t903tvzPj5y/YjUjuO77/7z+27d+5f0c7ecAr2e/Idsmp9O6La1h2327S+t+0i3ruml9PO2pyQl6OElaC8+lYDZcQAQMZGcAKUCkaMU5Y1w0c/V4sWoksjlW3T4nz9+abrnPzdI/JGsFtuaf/YJA/3SquDUFkuOmL7ctN3hkNTrVLehSUBQIS60soWMjrhSTzVk9unyZbz2NdlfZl2o3DUIno7RYLk1UNMItC3fe2nG0FzervCpDufoowUkc9abDUOT5GfL2dHGl7fPyPLSbdcvO2vkfyRrYt9x6/Nb/wCSVXovtw7Fz2J3bS1oOpRevum0nbtT957H7N7P7UFnuMb179uz6eRnP0RVr9lXyONK6q0RovUvaP24xNE3btMkvRNIWtyzOS/bFtuhoF11K0pb3rUkJB/dpOSTtpn4mSdJCn2fj4FNz2fkeJG4tyxtNsd9ASwTj7RXRg3JOFLhrB9ERhn9HFesr78l+6aY7G9b9p971Uyi4aH1s5oqVaWYZWh51Ab3PpkFYIGVkBPd8gZyM4Exn/IlMLtZ1R2Xf3TN/wBzegHNde3fMuPaNiWz7L3ff+DPeftm49Poc1X4yfQn9Mw8+8+Xp9zxpGtUv5jdursxQeK1MNQVJWp0p7tWXDxgDIAA6nPl5mZd5vjOlQiHq+4y5DraWHIT9rypKC2hBQl1WSEJSgDqOW0EDgKHrvS/yRew/UHY6e2ib8q02+yw341vup+4eQ77DcHW21mNxICnNpcA3pTtPXimx+SR2N6e7N9D9oXah8p86U+7yA5Pt8T7jH5vhbKQsb2pHl3iOqU53dODUeqnLw+ZUezccG08je/+L/Y8J+x3cdYUgj3tH9VaEWYThcB0eRwgir87KezRntZ7WNP9l0e//N7WoLmi3IuPsve92lRIDndbk7umdu4detP9UfJUNj7O+1ztKOu++/uW6wb0r7F817fnLc/3Xf8Aed8e5652bV+m7zo8Wd5Beq0MdNSu/gedk2ufu8MJ8g/6s0X06xdYFyaczLhNq8Ljwid9gdR4FDB5A+HXyq/e3zsLPyee0eL2enVQv++1wrn7Z7F7Jjv0btmzvHPo4xndz6CgEfp8KuOdttUVk0EeBPiu10IHPM5djjIOrX1KStrMRNoUgMgKQc7wkAgHJwOuznk1FWmJKpdzdX3z3etu7XXGylTpOecep6499W3qggaauSs4IZSQc453iqdWcq5JyaYnxIxLEsDpO/2+gP8Am+f/AAN78maz5vn/AMDe/ENPcnJ8VZk+vFSi7GfzdP8A4E/+Iaz5tuH8Bf8AyZp4FY9aOTF2mGYzblqC0vMIdUpLqgrJ645xQydMbix94m7pKvn8CL/Nlx/gL/5M1v5ruX8AkfkzRu6QW4hakxXC5Gkp3NqV1HqD7xQ8k/CrjUlaByReKXDI5gW24tyCpcF8Du3By2epQasqyT7pCssViL2kLgKCE74qrGVBB+8DO4IO4+DBJ5IZ6ndioQtKDp9t1LaQsSlJK8DJG3OM1uxYV7cFAKxDcUMjOCMc0Ll7LfQbHE3KML5qyxmL5fQ826vtbkNuJjS0pX8xKJSpa2yUHw87wNwP4KkcYzmq71bDuUyc28Li7eVKS6pbyIK2SlSpDqsEFI3FWe8z5d5j8GhW4+polp5xTd1aIOQQoEHoRtNFL2U2LxrvJqD2sC/NN1P/AGdJ9P2pX6qkekY9wgSpPf3KbaErYUhRFvL6Xhg+BSTxz05HmaSnRmlKaukAER3VjKfNpfmn4elNr2T87SuT+2mqjO2TNp6i+J9Pn4kqiy7szebu/G1lOZDzTAVLXbVZk4cScJT1SUnkfA1EJFuuzl3ekOMS5AWtau/WwpJcyD4iPImm2T61IJ8Yv+yPG6MRsxWvCtZB6deKkpcLSB0+m4uKS8un87Eahw5bcptTkZ1ICxnKCMc0vn31i8hRG8nnrnrXNGC9w3p/TUrUDcp2O+02iIApZX6HP6qOx+zCY+2HDd46EL+ipaCnOPTJGa77NGkPxb224lJSGEK5AOMbuef01Ysd9p2fKhtvqQ40lSWUhGMgfRAPw+0UjLklBqjfotNjzxk5rkVXe9DO2OC9LeuAcLKUqCe6wFgqCchWT60R0nJBsaG1qOWXFp59Dg/zmjGp1FejZyXErG1aAkKOcHekrA930PtqJW90QbI06lXL7i0kZ804/WKGMpZMdsPNjhpdSox5UG51zS2CEqFA5N0W4SEqwKYS5hOVOK+qhrspxzIScCrji6i8mob5DyTOVn6ZzTBclazyo1wEKUema6LK8ZKackkZHKUjSQ68sNtgqUo4AHU1KbX2X6xujS3hanGEhoOpL3h3Angc9OOeefdT7sqcgRL1JkT4qHXPZ9sbdglKyoZKRnJUEhR464I6kCpL2kN3W52Duodx79m3paW9GjOb0qBK/vnBy4gDYlK8BI2nzJAICupXFw045ZW1/O8mO3IAO2Mh0LWTwASU5AHJPJH0T6ihG0Vut7TUJRYQroeQrncEjJpNcttHU1yFFvkevc4w3kxwfjXpr/o7NKytQfKUgXmHahcndK2e43tuMSAFvJZLLIyeAe9fbwSRggHyryyZqT51ZvZJ2/3Dsi0f2hacslhjvz9e2dNkVc1vlDkCMSrvQhIHiKwoDqMbQecU2GNp2zLn1UJQcYvmewflCMdtfZB239hXbfa9HzLrryTpRqFd7Oy0Zb0qZEaLc0HuN+4LZkY3pzt27vKobqDSvY52oaCZ+UN2MWrUOin4GvLbB1JpeXNMiEt195sh1k9eFrHBwB4gEpATmm9FfLP1n2faQ0BZbBaIirz2d3mdc7ddpTqnu+jS0LTIhuNEDKF7/pBYUMcYOCHPbL8tP+6Doxjs/wCz3sr092b2Ry6t365NWdRW5OuKB4XSragISDghOCcpT4sDFG8b3ZkWoiqXz8r6HsTVBA+VP8r85/8AdYf/APIiVUvZVqbRnb8x2Z9gPyhOzzVOmtXRbWLfovWltcciOLhbFGPvaWkBxs93tDidwJ6FGVqqs9cf9IZqXWuib3aYvZtpeyaw1fbk2jVGrYbZ9qucJKNgbCCMNkoG1R3KGPohGE7XGgf+kDuekdJ2CHeeybSWo9X6Mt/zXpfVM1JTJt0UIKEoWgD77tSSAQpHHXJJJji7JDInHbnt49FRSFw0JeT2ryOzBUwS7sdQrsJkckOyPaSzv5OeVc9c819DPl6dnuqXvk567t9x0w5F0z2e33T8vSEsvNrU7B9iZgvoCUKKkJQ6pSiFAZ3A+XHzz7PO1n7kO1u0drV7taNRzLZdxe3I77/ciVKCy4lalgHGHdqzxzjHnUusHyrtQ2a79r8u7aeYvMLtegT4s2E/LUEQXX3FuNvNnaclouL2jAzwcjFDiVXY/UtzcXF3RLvl2ObNZdlX/wAJ9Of8r1Tbsi11oftH0d2V/J5+Ut2Watghc9UTQOtLY65DeaTLfRt2pWkIdQHFtguJ34Ts8ORuPmztv7apvbXd9L3abYmbUrTWlrdphCGny6HkRAsB4kgYKt/KecY61bvZV8uafofQ2ndJav7HtKa4uGhCtejbzddwkWdZVvGQAe8CVJQUhJbI2J8XAINrexat4lCtyydR6Wk6D+RB2xaJm3Ry5P2Dtnctrk1zIXJUy3HQXFZJ5VtyeT1q8L8f/va9qPP/ALgJP/049eMeyn5Yt90a7rG29o+grF2i6c1xdl6gulnug7tsXMq3e0NKCVBOTt3JKTkISAU45Wj/AC1NavdrOue1zUOnLdPna00pK0mIbLio8e3xXQ0lBb4UVbA0OCeSonIoNg+CbTvz+dfYmOiSP/s3u0PP/wCocL/6MWrI7ZLn2DwPkufJ8T2x6d1bc5julpwsq7HLZZbZV943l4OcqBJaxjyCvUV5NtHb9Ls3yeb/APJ9Tp9hyNfr+zfl3MyCHGlNobT3Yb24IPdA5z59KtzTvy6NIM9mmj+znXnyZ9I62b0XAMCDMu0oqWEnbvUlJaOzdtTkA/gipGO1eRc3vt1vn5EE+Ryof9aPszA//MMb9Jq3+00Y+TR8rX/4usf/AN4V5h7Lu1F3st7VrF2pQLMzMXYbmm5NQFOltteCSG9wBIHOM4ovq35UtyvPZ92p9nDmkIzbXahqxGqX5QmKKoK0v973KU7cLGeNxI+FMwRa2M/aEuP2i5f+kSx/1j4e4ZH3LWXIPn96NUj3QbOUDCFDKcdKd9uHbtN+UN2gx9f3DTzNldRbYds9mZkF5OGE7QvcQOTnOMcUzQouM7QfokkUWNe0ysrrFBeQN1aFK0xcQhOT3aTjH+mnNU8rknPUVc9+bff07cxHVgiKpec48IIJ/MDVLqUSTk555rTHkcrM7kaPrmtVlZRC0jKNXKFMmpt5jxluAxGwVBPA5PU9BQWlDJklAbMhwoAwElZxj4UMottNDsc4xjKMuTr+Qjd3WmYcS1tuBxcYKU4pJyApXOAfdQqsrKkY8KorLPvZcT/5WwUghE21uW0PIQ8l4PIC1bQvjBGfWlYsf5nalPTHmt7rCmW20LClEq8+Og4oMOPOsoXC9r2GxzJU63WyMp5aHm2Lg0484EITnKj5cGmdZRtcSoRCXBJSXgPrZcBBfKXUByM7hLqD0Iz1HvFc3d9mTcn5Ede5txW5Jxjy99M6yqUUpcQbyycO7fK7NhRCSnyJB6UVkP2iX3C335AU0yhpSUNjkj3k0JrKjipFY5vHaq7FJBYLyzGSoNZ8IUece+k6yszRC3vuSHSmoodianty2XnPakJSnuz9HG7k8jPWpA12lwGwkrtrz6207UrWAlW3GMEhXIx61X1YMnoDxQSxxnux2LPPCmoOrJtd9XwL3ZJrWFRnQlCGWT0I3gnbgeWPM1HVvts2iMEKJcU66VA+X0QPzUOQhKiB4lEnASmpUiwhFoiuTYmxbpWtI/0eAD+Y0WPEvdiVkzTzS4pO2RQqcfVg5PpTpiBxucokqAxHUShBpNSsHGOKk4T8AsfAt5HCGG0DgVKdH6SN0ubbV0s0p2M6gbVIIQlvJSQ44SQQnbkj90cYBoZpq3JutzLbrC3mYzLkp1tBwVpQnO3Plk4GfLOamek5mrNZX9T8mOh9RSk7XkrQzFjKGFd0AoABSfDjkkc5PNLjBp2xs8kWqiatkexaPkMSXFIm3eGVlTTSEIilzG8KDi0hRCUhPAyQoZ4J5d6eYRJuUrVcmeqMqA0pTsiTj2d9xwLK0NY4xlxQGAcA9Ny/DL7np+Fpy9W962Q3f2U13D6w4NwSkAFwqPIwFEkAeJRSeoqsbvp6+yn34cy9yZIQ6d6HJC3Ed4OCRk8855pyxzl7iE8UfFkIuLEJ2QowG1IZSAlG76SsDBUfQk848s499Me7xxk8VL3tGT2wSHEmh6tK3EqJwnrU7jMuaJJ45cmOZtw2nYlVMTIUo5JpotxS1FRPWt5wCRSI41FD82aWWVsdKk7R6mkFzVDqabLUR9dNVrUonNGkZ3NsdOTieNxPwpFUk9cUhWlGiSoW2xb2k+8V0JJ8lmmxGPrrVSicTHqZLiTlLh+2u/b3E+dMR0rk59arhQXeOPIKN3RQ4VTlq6t55NAQpWetbCjnNC8cWMjqckfEkqLg0rzFLCUhXRYqLhxQ4zXQkOo4CjQvCvAetbPxDEx1RPhVTP2iS0rcCSKbCU4eTzSiZCyOlWoNIS8qnJsetXPja4CPjQ6c4HXgoHzrtRCvKmrvCvhVxVMk8kpxpslFjc+hnPBFWOy4Q1lPBChiq0sZzsPvFWPHJ9mJ94oMa9qRpzSvFAMKYhuw3llslmRFdbdTnCUq2kEZ5x1zVDybXNYlri+zqKgsoG0Eg/A+dWlrG93Wy6XKrXNcjqelJQsoOCU7ScfmqtVao1EcA3qXwOMOkY+HpTkqMGR3IaG2XFJKTBfynr4DxSwsN5UjvE22QUn0Qa7RqC/uupQq+3Dxnaf2Svz+utXZ2Y1JLDk+Q8MAkuOE5NWDQmqy3VBCXILyCem9JGaxuzXBw7Uxzn3qA/TW5NtS1bYk8vKUZJOU46YUodf9mmz0dDbLLqScub859xwKhQ/b05PUAp1yMyknGXH0gVyixuuykRGpsJS1q2g9+kJ+OScYobisHOfdULDEnTioiC49d7YQD0blIcV9iSaS+bbYkILl9Z8fXa0pW3jzruw2yJcUyzKSslpAKNqsYJyOfXyP1UI8qFSTbXQlMfvxbW0oBq6F4eZDKh+mmxTHA4dUo48hik0I3+HOK0sbDt60RByy3b1Ae0SX0HPOxoKwPxhSnd2UO49qmlv1DCAfs30yByOlZUJQ9b+ZxnvUzVDPBG0frpdL2nEowqDPWr179I/o0hCjKnuohlwIABIIQCf7afxNONP2164LlK+97wEBPUpJHX6qhPITRM0slCSqyzlqycgzUgEeX4FOJGpLQ8juRpeOlsYGA4Ar8YJBqPEkGtdahWwSN1gB3e1Y4oSPwVrcV/SFLDUCUgBqx2xITyB3azz9a6Ysxm3IT0hW7e2QE4PFN0p5wPOqsJxapvxDbGqZEd1LzNptaFp8xHOemPWpHD1ZN1JAVGmwI6FRSCH2k7eDkBOPqP2VBXWu7kmPuzhW3OKN6WfW37dGGMLShZP8UqH9KmY/fRKpj+U1ycGmC2iKfvDJpo4MHFNmEiQdmtyiWjVjDtwAMeQ2uK4TjAC8dfs6fZVsXfWll0BNiWR5EmWhxpKkFvBLMfogEnhQBC8Y5wOcnk0EM5yCQRzRdWoLjNZiR7ipExuGSWg8CSB6ZBBxwOP7aTRbRdetnoGo9LxLxbrq0mK28HgTx3gwfD67h6e7npULiye+Ut9xI3OKKz8Sc1HX9Q3a7pZYmPpDLI2tstI2NoHoEjgDgfZRaHnuhzW7S+yJyIIPLaI5TTU93noK6cBA60gSc9a2uVmdRP/Z)
Maintenance is always going to be an issue you need to consider with your entire tech stack. You’ll need to make sure the language, libraries, and vendor support will stay current and updated for the long haul. At some point, you’ll likely need to transfer your codebase to another team.
Libraries offered by Java for AI programming is also a benefit. It offers these libraries:
It is a statically typed language which means type errors don’t appear during runtime. C++ also offers dynamic load balancing, adaptive caching, memory management and a host of other features that make it a highly favoured AI programming language. Because C++ is a statically typed language, type errors don’t appear during runtime. C++ also excels at dynamic load balancing, adaptive caching, and memory management—which is why this language is a top choice for building scalable big data frameworks.
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)
An exciting feature of Julia is that it can translate algorithms directly from research papers into code. Julia is a good language for big data and large-scale projects because it can be used to run on databases like Hadoop and Spark and can be easily distributed across multiple machines. Julia is a very high-performing language and is perfect for developers with a background in Python and R. Its most popular libraries are DataFrames, Distributions, and JuMP. Lisp is a family of programming languages with a long history and a distinctive, parenthesis-based syntax. It was originally created for artificial intelligence research.
The most benefits that C++ offers for AI app development:
The main advantage of C++ is its performance, efficiency, and flexibility as it was designed as a programming language for building resource-constrained software and large systems. Ever since its inception, the language has been used in desktop apps, web apps, networking apps, scientific computing, machine learning apps and data science applications. Python is a high level, general-purpose programming language.
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)
It is widely used by data miners and statisticians for statistical software and numerical analysis. R is an implementation of the S programming language combined with lexical scoping semantics and is inspired by Scheme. Python is used by a large number of companies and organizations across the world. It is extensively used in apps and programs in deep learning, AI, network security, numerical data processing, and scientific calculations.
LISP works perfectly for artificial intelligence projects that are heavy on ML because of its rapid prototyping capability and its automatic garbage collection feature. Think of LISP as the forerunner to the likes of Python, Java, and Julia. Created by budding electronics engineer John McCarthy in 1958, it is the second-oldest programming language that remains in use as a functional language today. Its name is simply an abbreviation of the term ‘list processing.’ Symbolic expressions were invented especially for LISP.
![machine 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It was one of the popular logic programming languages at that time with its usage in expert systems, theorem proving, type systems, and automated planning. Java has been consistently among the top programming languages in use for many years. Developed in 1995, Java is high-level, class-based, and object-oriented programming language that has created history with its write once, run anywhere principle. Let’s see how these languages are considered the best programming language for ai apps and solutions.
Is C++ good for AI?
Yes, C++ is good for artificial intelligence. C++ is a flexible programming language based on object oriented principles, meaning it can be used for AI. The syntax of the programming language is not easy to understand, however, making it hard to learn, especially for beginners.
Speaking of which, application scaling is very important when it comes to choosing programming languages for AI development. Scaling offers high cross-functionality and portability for the programs written once to run across multiple platforms such as desktops, mobiles, embedded systems et al. Python is currently the best language for research and development in machine learning with great community support. According to google trends, for the past 5 years, interest in Python language among developers has spiked, when compared to other languages such as java, scala, R, prolog, Julia, etc. When it comes to statistical programming, R is the language that comes first into the mind. R is very popular for data analysis and statistical computing.
5 Best Machine Learning (AI) Programming Languages (2023) – Unite.AI
5 Best Machine Learning (AI) Programming Languages ( .
Posted: Tue, 12 Jul 2022 07:00:00 GMT [source]